Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based ...Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based on the 3D U-Net architecture for liver region segmentation. Our model replaces the last two layers of the 3D U-Net with a sliding window-based multilayer perceptron(SMLP), enabling better extraction of local image features. We also design a high-and low-level feature fusion dilated convolution block that focuses on local features and better supplements the surrounding information of the target region. This block is embedded in the entire encoding process, ensuring that the overall network is not simply downsampling. Before each feature extraction, the input features are processed by the dilated convolution block. We validate our experiments on the liver tumor segmentation challenge 2017(Lits2017) dataset, and our model achieves a Dice coefficient of 0.95, which is an improvement of 0.015 compared to the 3D U-Net model. Furthermore, we compare our results with other segmentation methods, and our model consistently outperforms them.展开更多
BACKGROUND Split liver transplantation(SLT)is a complex procedure.The left-lateral and right tri-segment splits are the most common surgical approaches and are based on the Couinaud liver segmentation theory.Notably,t...BACKGROUND Split liver transplantation(SLT)is a complex procedure.The left-lateral and right tri-segment splits are the most common surgical approaches and are based on the Couinaud liver segmentation theory.Notably,the liver surface following right trisegment splits may exhibit different degrees of ischemic changes related to the destruction of the local portal vein blood flow topology.There is currently no consensus on preoperative evaluation and predictive strategy for hepatic segmental necrosis after SLT.AIM To investigate the application of the topological approach in liver segmentation based on 3D visualization technology in the surgical planning of SLT.METHODS Clinical data of 10 recipients and 5 donors who underwent SLT at Shenzhen Third People’s Hospital from January 2020 to January 2021 were retrospectively analyzed.Before surgery,all the donors were subjected to 3D modeling and evaluation.Based on the 3D-reconstructed models,the liver splitting procedure was simulated using the liver segmentation system described by Couinaud and a blood flow topology liver segmentation(BFTLS)method.In addition,the volume of the liver was also quantified.Statistical indexes mainly included the hepatic vasculature and expected volume of split grafts evaluated by 3D models,the actual liver volume,and the ischemia state of the hepatic segments during the actual surgery.RESULTS Among the 5 cases of split liver surgery,the liver was split into a left-lateral segment and right trisegment in 4 cases,while 1 case was split using the left and right half liver splitting.All operations were successfully implemented according to the preoperative plan.According to Couinaud liver segmentation system and BFTLS methods,the volume of the left lateral segment was 359.00±101.57 mL and 367.75±99.73 mL,respectively,while that measured during the actual surgery was 397.50±37.97 mL.The volume of segment IV(the portion of ischemic liver lobes)allocated to the right tri-segment was 136.31±86.10 mL,as determined using the topological approach to liver segmentation.However,during the actual surgical intervention,ischemia of the right tri-segment section was observed in 4 cases,including 1 case of necrosis and bile leakage,with an ischemic liver volume of 238.7 mL.CONCLUSION 3D visualization technology can guide the preoperative planning of SLT and improve accuracy during the intervention.The simulated operation based on 3D visualization of blood flow topology may be useful to predict the degree of ischemia in the liver segment and provide a reference for determining whether the ischemic liver tissue should be removed during the surgery.展开更多
Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neura...Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neural Networks （CNN） is presented in this paper. It ensures the validity of this scheme and at the same time completes the im-age segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN irmge process-hag, firstly, three-dimensional liver MRI images should be transformed into binary images; second- ly, an appropriate template parameter of the Global Connectivity Detection CNN （GCD CNN） shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN （HF CNN） are used to repair the entire extracting liver and improve the accuracy of fiver segmentation; final-ly, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic seg-mentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.展开更多
Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases.In recent years,due to the great improvement of hard device,many deep learning based m...Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases.In recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver segmentation.Among them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many variations.They have achieved certain achievements in medical image segmentation.In this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver segmentation.Since original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live segmentation.Our experimental results show that Densenet performs the best on liver segmentation,followed by Resnet.Both perform much better than Segnet,U-Net,and FCN.Among Segnet,U-Net,and FCN,U-Net performs the best,followed by Segnet.FCN performs the worst.展开更多
Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address t...Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.展开更多
Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(D...Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.展开更多
Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In thi...Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation. We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.展开更多
Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmenta...Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.展开更多
This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer;second, a spatial f...This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer;second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically;thirdly, a distance regularized level set is used for refinement;finally, morphological operations are used as post-processing. The experiment result shows that the method can achieve high accuracy (0.9986) and specificity (0.9989). Comparing with standard level set method, our method is more effective in dealing with over-segmentation problem.展开更多
The liver has eight segments, which are referred to by numbers or by names. The numbering of the segments is done in a counterclockwise manner with the liver being viewed from the inferior surface, starting from Segme...The liver has eight segments, which are referred to by numbers or by names. The numbering of the segments is done in a counterclockwise manner with the liver being viewed from the inferior surface, starting from Segment Ⅰ(the caudate lobe). Standard anatomical description of the liver segments is available by computed tomographic scan and ultrasonography. Endoscopic ultrasound(EUS) has been used for a detailed imaging of many intra-abdominal organs and for the assessment of intra-abdominal vasculature. A stepwise evaluation of the liver segments by EUS has not been described. In this article, we have described a stepwise evaluation of the liver segments by EUS. This information can be useful for planning successful radical surgeries, preparing for biopsy, portal vein embolization, transjugular intrahepatic portosystemic shunt, tumour resection or partial hepatectomy, and for planning EUS guided diagnostic and therapeutic procedures.展开更多
BACKGROUND Hepatocellular carcinoma(HCC)in segments VII and VIII poses technical challenges for both liver resection and radiofrequency ablation(RFA).Robotic-assisted techniques may enhance safety and precision,but co...BACKGROUND Hepatocellular carcinoma(HCC)in segments VII and VIII poses technical challenges for both liver resection and radiofrequency ablation(RFA).Robotic-assisted techniques may enhance safety and precision,but comparative evidence remains limited.AIM To compare the clinical outcomes of robotic liver resection(R-LR)and robotic intraoperative RFA(RIO-RFA)for HCC located in liver segments VII and VIII.METHODS We retrospectively analyzed 93 HCC patients in segments VII/VIII with de novo(n=57)or first recurrent(n=36).HCC who underwent R-LR or RIO-RFA between 2015 and 2024.Propensity score matching was performed to reduce selection bias.Primary outcomes were overall survival(OS)and recurrence-free survival(RFS).Kaplan-Meier curves,log-rank tests,and Cox regression were used to identify prognostic factors for OS and RFS.RESULTS In the de novo group,OS and RFS did not differ significantly between R-LR and RIO-RFA before or after propensity score matching.In contrast,the recurrent group showed significantly improved OS and RFS with R-LR(P=0.005 and P=0.012,respectively).Subgroup analyses revealed that low-risk de novo patients with smaller tumors achieved superior OS after R-LR,whereas carefully selected low-risk recurrent patients undergoing RIO-RFA(smaller tumors,absence of complications)achieved outcomes comparable to R-LR.Platelet count,tumor size,and postoperative complications constituted key prognostic factors.CONCLUSION For HCC in challenging liver segments VII and VIII,R-LR and RIO-RFA achieve comparable outcomes in de novo cases,whereas R-LR confers superior survival in recurrent disease.R-LR should be prioritized for small de novo HCCs and for recurrent disease overall;RIO-RFA may serve as an effective alternative in carefully selected lowrisk recurrent patients.Tumor size,platelet count,and postoperative complications are key prognostic indicators to guide individualized treatment.展开更多
Automatic segmentation of the liver and hepatic lesions from abdominal 3D comput-ed tomography(CT)images is fundamental tasks in computer-assisted liver surgery planning.However,due to complex backgrounds,ambiguous bo...Automatic segmentation of the liver and hepatic lesions from abdominal 3D comput-ed tomography(CT)images is fundamental tasks in computer-assisted liver surgery planning.However,due to complex backgrounds,ambiguous boundaries,heterogeneous appearances and highly varied shapes of the liver,accurate liver segmentation and tumor detection are stil-1 challenging problems.To address these difficulties,we propose an automatic segmentation framework based on 3D U-net with dense connections and globally optimized refinement.First-ly,a deep U-net architecture with dense connections is trained to learn the probability map of the liver.Then the probability map goes into the following refinement step as the initial surface and prior shape.The segmentation of liver tumor is based on the similar network architecture with the help of segmentation results of liver.In order to reduce the infuence of the surrounding tissues with the similar intensity and texture behavior with the tumor region,during the training procedure,I x liverlabel is the input of the network for the segmentation of liver tumor.By do-ing this,the accuracy of segmentation can be improved.The proposed method is fully automatic without any user interaction.Both qualitative and quantitative results reveal that the pro-posed approach is efficient and accurate for liver volume estimation in clinical application.The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.展开更多
Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it posses...Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.展开更多
Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, het...Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algorithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, and contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross validation. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.展开更多
Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and sus...Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective errors.To address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet architecture.The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,respectively.Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input globally.And the model uses deep supervision and a hybrid loss function for faster convergence.On the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.展开更多
This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and s...This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.展开更多
In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and...In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the Liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method.展开更多
The second-leading cause of cancer-related deaths globally is liver cancer.The treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT scans.The improved method based on U-Net h...The second-leading cause of cancer-related deaths globally is liver cancer.The treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT scans.The improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be improved.To deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale features.We propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature information.Finally,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84%for liver tumor segmentation,which were 2.89 and 7.89%higher than the baseline model,respectively.The experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor segmentation.We also extended MAPFUNet to brain tumor segmentation on the BraTS2019 dataset.The results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27%(WT),84.77%(TC),and 76.98%(ET),respectively.展开更多
<strong>Purpose</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong></span><span style=&q...<strong>Purpose</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong></span><span style="font-family:Verdana;">To improve the liver auto-segmentation performance of three-</span><span style="font-family:Verdana;">dimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers. The hypothesis of this study was that the segmentation quality of </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL). Seventy</span><span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;">eight plans of abdominal cancer patients were anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen plans were used to test the performance. The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks. The segmentation performance was evaluated using the remaining 16 cases. For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN. </span><b><span style="font-family:Verdana;">Conclusion</span></b><span style="font-family:Verdana;">: The PDN has outperformed the U-Net on liver auto-segmentation. The predicted contours of PDN are more conformal and smoother when compared with</span></span><span style="font-family:Verdana;"> the</span><span style="font-family:Verdana;"> U-Net.</span>展开更多
Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present wi...Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.展开更多
基金supported by the Shandong Provincial Natural Science Foundation (Nos.ZR2023MF062 and ZR2021MF115)the Introduction and Cultivation Program for Young Innovative Talents of Universities in Shandong (No.2021QCYY003)。
文摘Considering the three-dimensional(3D) U-Net lacks sufficient local feature extraction for image features and lacks attention to the fusion of high-and low-level features, we propose a new model called 3DMAU-Net based on the 3D U-Net architecture for liver region segmentation. Our model replaces the last two layers of the 3D U-Net with a sliding window-based multilayer perceptron(SMLP), enabling better extraction of local image features. We also design a high-and low-level feature fusion dilated convolution block that focuses on local features and better supplements the surrounding information of the target region. This block is embedded in the entire encoding process, ensuring that the overall network is not simply downsampling. Before each feature extraction, the input features are processed by the dilated convolution block. We validate our experiments on the liver tumor segmentation challenge 2017(Lits2017) dataset, and our model achieves a Dice coefficient of 0.95, which is an improvement of 0.015 compared to the 3D U-Net model. Furthermore, we compare our results with other segmentation methods, and our model consistently outperforms them.
基金The Third People's Hospital of Shenzhen Scientific Research Project,No.G2021008 and No.G2022008Shenzhen Key Medical Discipline Construction Fund,No.SZXK079Shenzhen Science and Technology Research and Development Fund,No.JCYJ20190809165813331 and No.JCYJ20210324131809027。
文摘BACKGROUND Split liver transplantation(SLT)is a complex procedure.The left-lateral and right tri-segment splits are the most common surgical approaches and are based on the Couinaud liver segmentation theory.Notably,the liver surface following right trisegment splits may exhibit different degrees of ischemic changes related to the destruction of the local portal vein blood flow topology.There is currently no consensus on preoperative evaluation and predictive strategy for hepatic segmental necrosis after SLT.AIM To investigate the application of the topological approach in liver segmentation based on 3D visualization technology in the surgical planning of SLT.METHODS Clinical data of 10 recipients and 5 donors who underwent SLT at Shenzhen Third People’s Hospital from January 2020 to January 2021 were retrospectively analyzed.Before surgery,all the donors were subjected to 3D modeling and evaluation.Based on the 3D-reconstructed models,the liver splitting procedure was simulated using the liver segmentation system described by Couinaud and a blood flow topology liver segmentation(BFTLS)method.In addition,the volume of the liver was also quantified.Statistical indexes mainly included the hepatic vasculature and expected volume of split grafts evaluated by 3D models,the actual liver volume,and the ischemia state of the hepatic segments during the actual surgery.RESULTS Among the 5 cases of split liver surgery,the liver was split into a left-lateral segment and right trisegment in 4 cases,while 1 case was split using the left and right half liver splitting.All operations were successfully implemented according to the preoperative plan.According to Couinaud liver segmentation system and BFTLS methods,the volume of the left lateral segment was 359.00±101.57 mL and 367.75±99.73 mL,respectively,while that measured during the actual surgery was 397.50±37.97 mL.The volume of segment IV(the portion of ischemic liver lobes)allocated to the right tri-segment was 136.31±86.10 mL,as determined using the topological approach to liver segmentation.However,during the actual surgical intervention,ischemia of the right tri-segment section was observed in 4 cases,including 1 case of necrosis and bile leakage,with an ischemic liver volume of 238.7 mL.CONCLUSION 3D visualization technology can guide the preoperative planning of SLT and improve accuracy during the intervention.The simulated operation based on 3D visualization of blood flow topology may be useful to predict the degree of ischemia in the liver segment and provide a reference for determining whether the ischemic liver tissue should be removed during the surgery.
基金supported by the National Natural Science Foundation of China under Grant No. 61074192the Funds of the USTB under Grants No. YJ2010-019,No.06108104
文摘Currently, the processing speed of exist-ing autormtic liver segmentation for Magnetic Res-onance Imaging （MRI） images is rehtively slow. An automatic liver segmentation scheme for MRI irmges based on Cellular Neural Networks （CNN） is presented in this paper. It ensures the validity of this scheme and at the same time completes the im-age segmentation faster to accurately calculate the liver volume by using parallel computing in real time. In order to facilitate the CNN irmge process-hag, firstly, three-dimensional liver MRI images should be transformed into binary images; second- ly, an appropriate template parameter of the Global Connectivity Detection CNN （GCD CNN） shall be selected to probe the connectivity of the liver to extract the entire liver; and then the Hole-Filler CNN （HF CNN） are used to repair the entire extracting liver and improve the accuracy of fiver segmentation; final-ly, the liver volume is obtained. Results show that the scheme can ensure the accuracy of the automatic seg-mentation of the liver, and it can also improve the processing speed at the same time. The liver volume calculated is in line with the clinical diagnosis.
基金This research has been partially supported by National Science Foundation under grant IIS-1115417the National Natural Science Foundation of China under grant 61728205,61876217+1 种基金the“double first-class”international cooperation and development scientific research project of Changsha University of Science and Technology(No.2018IC25)the Science and Technology Development Project of Suzhou under grant SZS201609 and SYG201707.
文摘Accurate segmentation of CT images of liver tumors is an important adjunct for the liver diagnosis and treatment of liver diseases.In recent years,due to the great improvement of hard device,many deep learning based methods have been proposed for automatic liver segmentation.Among them,there are the plain neural network headed by FCN and the residual neural network headed by Resnet,both of which have many variations.They have achieved certain achievements in medical image segmentation.In this paper,we firstly select five representative structures,i.e.,FCN,U-Net,Segnet,Resnet and Densenet,to investigate their performance on liver segmentation.Since original Resnet and Densenet could not perform image segmentation directly,we make some adjustments for them to perform live segmentation.Our experimental results show that Densenet performs the best on liver segmentation,followed by Resnet.Both perform much better than Segnet,U-Net,and FCN.Among Segnet,U-Net,and FCN,U-Net performs the best,followed by Segnet.FCN performs the worst.
基金the Project of China Scholarship Council(No.201708615011)the Xi’an Science and Technology Plan Project(No.GXYD1.7)。
文摘Convolutional neural networks(CNNs)are prone to mis-segmenting image data of the liver when the background is complicated,which results in low segmentation accuracy and unsuitable results for clinical use.To address this shortcoming,an interactive liver segmentation algorithm based on geodesic distance and V-net is proposed.The three-dimensional segmentation network V-net adequately considers the characteristics of the spatial context information to segment liver medical images and obtain preliminary segmentation results.An artificial algorithm based on geodesic distance is used to form artificial hard constraints to modify the image,and the superpixel piece created by the watershed algorithm is introduced as a sample point for operation,which significantly improves the efficiency of segmentation.Results from simulation of the liver tumor segmentation challenge(LiTS)dataset show that this algorithm can effectively refine the results of automatic liver segmentation,reduce user intervention,and enable a fast,interactive liver image segmentation that is convenient for doctors.
文摘Liver segmentation in CT images is an important step for liver volumetry and vascular evaluation in liver pre-surgical planning. In this paper, a segmentation method based on distance regularized level set evolution(DRLSE) model was proposed, which incorporated a distance regularization term into the conventional Chan-Vese (C-V) model. In addition, the region growing method was utilized to generate the initial liver mask for each slice, which could decrease the computation time for level-set propagation. The experimental results show that the method can dramatically decrease the evolving time and keep the accuracy of segmentation. The new method is averagely 15 times faster than the method based on conventional C-V model in segmenting a slice.
文摘Automatic liver segmentation from abdominal images is challenging on the aspects of segmentation accuracy, automation and robustness. There exist many methods of liver segmentation and ways of categorisingthem. In this paper, we present a new way of summarizing the latest achievements in automatic liver segmentation. We categorise a segmentation method according to the image feature it works on, therefore better summarising the performance of each category and leading to finding an optimal solution for a particular segmentation task. All the methods of liver segmentation are categorized into three main classes including gray level based method, structure based method and texture based method. In each class, the latest advance is reviewed with summary comments on the advantages and drawbacks of each discussed approach. Performance comparisons among the classes are given along with the remarks on the problems existed and possible solutions. In conclusion, we point out that liver segmentation is still an open issue and the tendency is that multiple methods will be employed together to achieve better segmentation performance.
文摘Automatic segmentation of liver in medical images is challenging on the aspects of accuracy, automation and robustness. A crucial stage of the liver segmentation is the selection of the image features for the segmentation. This paper presents an accurate liver segmentation algorithm. The approach starts with a texture analysis which results in an optimal set of texture features including high order statistical texture features and anatomical structural features. Then, it creates liver distribution image by classifying the original image pixelwisely using support vector machines. Lastly, it uses a group of morphological operations to locate the liver organ accurately in the image. The novelty of the approach is resided in the fact that the features are so selected that both local and global texture distributions are considered, which is important in liver organ segmentation where neighbouring tissues and organs have similar greyscale distributions. Experiment results of liver segmentation on CT images using the proposed method are presented with performance validation and discussion.
文摘This paper presents a fully automatic segmentation method of liver CT scans using fuzzy c-mean clustering and level set. First, the contrast of original image is enhanced to make boundaries clearer;second, a spatial fuzzy c-mean clustering combining with anatomical prior knowledge is employed to extract liver region automatically;thirdly, a distance regularized level set is used for refinement;finally, morphological operations are used as post-processing. The experiment result shows that the method can achieve high accuracy (0.9986) and specificity (0.9989). Comparing with standard level set method, our method is more effective in dealing with over-segmentation problem.
文摘The liver has eight segments, which are referred to by numbers or by names. The numbering of the segments is done in a counterclockwise manner with the liver being viewed from the inferior surface, starting from Segment Ⅰ(the caudate lobe). Standard anatomical description of the liver segments is available by computed tomographic scan and ultrasonography. Endoscopic ultrasound(EUS) has been used for a detailed imaging of many intra-abdominal organs and for the assessment of intra-abdominal vasculature. A stepwise evaluation of the liver segments by EUS has not been described. In this article, we have described a stepwise evaluation of the liver segments by EUS. This information can be useful for planning successful radical surgeries, preparing for biopsy, portal vein embolization, transjugular intrahepatic portosystemic shunt, tumour resection or partial hepatectomy, and for planning EUS guided diagnostic and therapeutic procedures.
基金Supported by Feng Chia University/Chung Shan Medical University,No.FCU/CSMU 112-001Taiwan National Science and Technology Council,No.NSTC 114-2221-E-035-036.
文摘BACKGROUND Hepatocellular carcinoma(HCC)in segments VII and VIII poses technical challenges for both liver resection and radiofrequency ablation(RFA).Robotic-assisted techniques may enhance safety and precision,but comparative evidence remains limited.AIM To compare the clinical outcomes of robotic liver resection(R-LR)and robotic intraoperative RFA(RIO-RFA)for HCC located in liver segments VII and VIII.METHODS We retrospectively analyzed 93 HCC patients in segments VII/VIII with de novo(n=57)or first recurrent(n=36).HCC who underwent R-LR or RIO-RFA between 2015 and 2024.Propensity score matching was performed to reduce selection bias.Primary outcomes were overall survival(OS)and recurrence-free survival(RFS).Kaplan-Meier curves,log-rank tests,and Cox regression were used to identify prognostic factors for OS and RFS.RESULTS In the de novo group,OS and RFS did not differ significantly between R-LR and RIO-RFA before or after propensity score matching.In contrast,the recurrent group showed significantly improved OS and RFS with R-LR(P=0.005 and P=0.012,respectively).Subgroup analyses revealed that low-risk de novo patients with smaller tumors achieved superior OS after R-LR,whereas carefully selected low-risk recurrent patients undergoing RIO-RFA(smaller tumors,absence of complications)achieved outcomes comparable to R-LR.Platelet count,tumor size,and postoperative complications constituted key prognostic factors.CONCLUSION For HCC in challenging liver segments VII and VIII,R-LR and RIO-RFA achieve comparable outcomes in de novo cases,whereas R-LR confers superior survival in recurrent disease.R-LR should be prioritized for small de novo HCCs and for recurrent disease overall;RIO-RFA may serve as an effective alternative in carefully selected lowrisk recurrent patients.Tumor size,platelet count,and postoperative complications are key prognostic indicators to guide individualized treatment.
基金Supported by the National Natural Science Foundation of China(12090020,12090025)Zhejiang Provin-cial Natural Science Foundation of China(LSD19H180005)。
文摘Automatic segmentation of the liver and hepatic lesions from abdominal 3D comput-ed tomography(CT)images is fundamental tasks in computer-assisted liver surgery planning.However,due to complex backgrounds,ambiguous boundaries,heterogeneous appearances and highly varied shapes of the liver,accurate liver segmentation and tumor detection are stil-1 challenging problems.To address these difficulties,we propose an automatic segmentation framework based on 3D U-net with dense connections and globally optimized refinement.First-ly,a deep U-net architecture with dense connections is trained to learn the probability map of the liver.Then the probability map goes into the following refinement step as the initial surface and prior shape.The segmentation of liver tumor is based on the similar network architecture with the help of segmentation results of liver.In order to reduce the infuence of the surrounding tissues with the similar intensity and texture behavior with the tumor region,during the training procedure,I x liverlabel is the input of the network for the segmentation of liver tumor.By do-ing this,the accuracy of segmentation can be improved.The proposed method is fully automatic without any user interaction.Both qualitative and quantitative results reveal that the pro-posed approach is efficient and accurate for liver volume estimation in clinical application.The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and non-reproducible manual segmentation method.
文摘Liver Segmentation is one of the challenging tasks in detecting and classifying liver tumors from Computed Tomography(CT)images.The segmentation of hepatic organ is more intricate task,owing to the fact that it possesses a sizeable quantum of vascularization.This paper proposes an algorithm for automatic seed point selection using energy feature for use in level set algorithm for segmentation of liver region in CT scans.The effectiveness of the method can be determined when used in a model to classify the liver CT images as tumorous or not.This involves segmentation of the region of interest(ROI)from the segmented liver,extraction of the shape and texture features from the segmented ROI and classification of the ROIs as tumorous or not by using a classifier based on the extracted features.In this work,the proposed seed point selection technique has been used in level set algorithm for segmentation of liver region in CT scans and the ROIs have been extracted using Fuzzy C Means clustering(FCM)which is one of the algorithms to segment the images.The dataset used in this method has been collected from various repositories and scan centers.The outcome of this proposed segmentation model has reduced the area overlap error that could offer the intended accuracy and consistency.It gives better results when compared with other existing algorithms.Fast execution in short span of time is another advantage of this method which in turns helps the radiologist to ascertain the abnormalities instantly.
文摘Liver tumors segmentation from computed tomography (CT) images is an essential task for diagnosis and treatments of liver cancer. However, it is difficult owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions. In this paper, an automatic method based on convolutional neural networks (CNNs) is presented to segment lesions from CT images. The CNNs is one of deep learning models with some convolutional filters which can learn hierarchical features from data. We compared the CNNs model to popular machine learning algorithms: AdaBoost, Random Forests (RF), and support vector machine (SVM). These classifiers were trained by handcrafted features containing mean, variance, and contextual features. Experimental evaluation was performed on 30 portal phase enhanced CT images using leave-one-out cross validation. The average Dice Similarity Coefficient (DSC), precision, and recall achieved of 80.06% ± 1.63%, 82.67% ± 1.43%, and 84.34% ± 1.61%, respectively. The results show that the CNNs method has better performance than other methods and is promising in liver tumor segmentation.
文摘Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective errors.To address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet architecture.The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,respectively.Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input globally.And the model uses deep supervision and a hybrid loss function for faster convergence.On the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.
文摘This paper presents an efficient liver-segmentation system developed by combining three ideas under the operations of a level-set method and consequent processes. First, an effective initial process creates mask and seed regions. The mask regions assist in prevention of leakage regions due to an overlap of gray-intensities between liver and another soft-tissue around ribs and verte-brae. The seed regions are allocated inside the liver to measure statistical values of its gray-intensities. Second, we introduce liver-corrective images to represent statistical regions of the liver and preserve edge information. These images help a geodesic active contour (GAC) to move without obstruction from high level of image noises. Lastly, the computation time in a level-set based on reaction-diffusion evolution and the GAC method is reduced by using a concept of multi-resolution. We applied the proposed system to 40 sets of 3D CT-liver data, which were acquired from four patients (10 different sets per patient) by a 4D-CT imaging system. The segmentation results showed 86.38% ± 4.26% (DSC: 91.38% ± 2.99%) of similarities to outlines of manual delineation provided by a radiologist. Meanwhile, the results of liver segmentation only using edge images presented 79.17% ± 5.15% or statistical regions showed 74.04% ± 9.77% of similarities.
基金funded by Dirección General de Investigaciones of Universidad Santiago de Cali under call No.01-2021.
文摘In the recent days, the segmentation of Liver Tumor (LT) has beendemanding and challenging. The process of segmenting the liver and accuratelyspotting the tumor is demanding due to the diversity of shape, texture, and intensity of the liver image. The intensity similarities of the neighboring organs of theliver create difficulties during liver segmentation. The manual segmentation doesnot provide an accurate segmentation because the results provided by differentmedical experts can vary. Also, this manual technique requires a large numberof image slices and time for segmentation. To solve these issues, the Fully Automatic Segmentation (FAS) technique is proposed. In this proposed Multi-AngleTexture Active Contour Model (MAT-ACM) method, the input Computed Tomography (CT) image is preprocessed by Contrast Enhancement (CE) with Non-Linear Mapping Technique (NLMT), in which the liver is differentiated from itsneighbouring soft tissues with related strength. Then, the filtered images are givenas the input to Adaptive Edge Modeling (AEM) with Canny Edge Detection(CED) technique, which segments the Liver Region (LR) from the given CTimages. An AEM with a CED model is implemented, which increases the convergence speed of the iterative process for decreasing the Volumetric Overlap Error(VOE) is 6.92% rates when compared with the traditional Segmentation Techniques (ST). Finally, the Liver Tumor Segmentation (LTS) is developed by applyingthe MAT-ACM, which accurately segments the LR from the segmented LRs. Theevaluation of the proposed method is compared with the existing LTS methodsusing various performance measures to prove the superiority of the proposedMAT-ACM method.
基金supported by the National Natural Science Foundation of China(62276092)Key Science and Technology Program of Henan Province(212102310084)+11 种基金Key Scientific Research Projects of Colleges and Universities in Henan Province(22A520027)MRC,UK(MC_PC_17171)Royal Society,UK(RP202G0230)BHF,UK(AA/18/3/34220)Hope Foundation for Cancer Research,UK(RM60G0680)GCRF,UK(P202PF11)SinoUK Industrial Fund,UK(RP202G0289)LIAS,UK(P202ED10,P202RE969)Data Science Enhancement Fund,UK(P202RE237)Fight for Sight,UK(24NN201)Sino-UK Education Fund,UK(OP202006)BBSRC,UK(RM32G0178B8).
文摘The second-leading cause of cancer-related deaths globally is liver cancer.The treatment of liver cancers depends heavily on the accurate segmentation of liver tumors from CT scans.The improved method based on U-Net has achieved good perfor-mance for liver tumor segmentation,but these methods can still be improved.To deal with the problems of poor performance from the original U-Net framework in the segmentation of small-sized liver tumors and the position information of tumors that is seriously lost in the down-sampling process,we propose the Multi-attention Perception-fusion U-Net(MAPFU-Net).We propose the Position ResBlock(PResBlock)in the encoder stage to promote the feature extraction capability of MAPFUNet while retaining the position information regarding liver tumors.A Dual-branch Attention Module(DWAM)is proposed in the skip connections,which narrows the semantic gap between the encoder's and decoder's features and enables the network to utilize the encoder's multi-stage and multi-scale features.We propose the Channel-wise ASPP with Atten-tion(CAA)module at the bottleneck,which can be combined with multi-scale features and contributes to the recovery of micro-tumor feature information.Finally,we evaluated MAPFUNet on the LITS2017 dataset and the 3DIRCADB-01 dataset,with Dice values of 85.81 and 83.84%for liver tumor segmentation,which were 2.89 and 7.89%higher than the baseline model,respectively.The experiment results show that MAPFUNet is superior to other networks with better tumor feature representation and higher accuracy of liver tumor segmentation.We also extended MAPFUNet to brain tumor segmentation on the BraTS2019 dataset.The results indicate that MAPFUNet performs well on the brain tumor segmentation task,and its Dice values on the three tumor regions are 83.27%(WT),84.77%(TC),and 76.98%(ET),respectively.
文摘<strong>Purpose</strong><span style="font-family:;" "=""><span style="font-family:Verdana;"><strong>: </strong></span><span style="font-family:Verdana;">To improve the liver auto-segmentation performance of three-</span><span style="font-family:Verdana;">dimensional (3D) U-net by replacing the conventional up-sampling convolution layers with the Pixel De-convolutional Network (PDN) that considers spatial features. </span><b><span style="font-family:Verdana;">Methods</span></b><span style="font-family:Verdana;">: The U-net was originally developed to segment neuronal structure with outstanding performance but suffered serious artifacts from indirectly unrelated adjacent pixels in its up-sampling layers. The hypothesis of this study was that the segmentation quality of </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">liver could be improved with PDN in which the up-sampling layer was replaced by a pixel de-convolution layer (PDL). Seventy</span><span style="font-family:Verdana;">-</span><span style="font-family:;" "=""><span style="font-family:Verdana;">eight plans of abdominal cancer patients were anonymized and exported. Sixty-two were chosen for training two networks: 1) 3D U-Net, and 2) 3D PDN, by minimizing the Dice loss function. The other sixteen plans were used to test the performance. The similarity Dice and Average Hausdorff Distance (AHD) were calculated and compared between these two networks. </span><b><span style="font-family:Verdana;">Results</span></b><span style="font-family:Verdana;">: The computation time for 62 training cases and 200 training epochs was about 30 minutes for both networks. The segmentation performance was evaluated using the remaining 16 cases. For the Dice score, the mean ± standard deviation were 0.857 ± 0.011 and 0.858 ± 0.015 for the PDN and U-Net, respectively. For the AHD, the mean ± standard deviation were 1.575 ± 0.373 and 1.675 ± 0.769, respectively, corresponding to an improvement of 6.0% and 51.5% of mean and standard deviation for the PDN. </span><b><span style="font-family:Verdana;">Conclusion</span></b><span style="font-family:Verdana;">: The PDN has outperformed the U-Net on liver auto-segmentation. The predicted contours of PDN are more conformal and smoother when compared with</span></span><span style="font-family:Verdana;"> the</span><span style="font-family:Verdana;"> U-Net.</span>
基金the“Intelligent Recognition Industry Service Center”as part of the Featured Areas Research Center Program under the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan,and the National Science and Technology Council,Taiwan,under grants 113-2221-E-224-041 and 113-2622-E-224-002.Additionally,partial support was provided by Isuzu Optics Corporation.
文摘Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.
