This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualiz...This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.展开更多
Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learni...Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.展开更多
Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morp...Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morphology,and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task.To address these challenges,we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images.First,a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network,enhancing pancreatic feature extraction and improving localization accuracy.Second,a Multi-Scale Feature Extraction module is embedded into the network to expand the receptive field while preserving feature map resolution,mitigate feature degradation caused by network depth,and maintain awareness of pancreatic anatomical structures.Third,a Dimensional Squeeze Attention module is designed to reduce interference from adjacent organs and highlight useful pancreatic features through spatial-channel interaction,thereby enhancing sensitivity to small targets.Finally,a hybrid loss function combining Dice loss and Focal loss is employed to alleviate class imbalance issues.Extensive evaluation on two public datasets(NIH and MSD)shows that the proposed RPMS-DSAUnet achieves Dice Similarity Coefficients of 85.51%and 80.91%,with corresponding Intersection over Union(IoU)scores of 74.93%and 67.94%on each dataset,respectively.Experimental results demonstrate superior performance of the proposed model over baseline methods and state-of-the-art approaches,validating its effectiveness for CT-based pancreas segmentation.展开更多
We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM...We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.展开更多
Objective:The aim of our study was to investigate the value of CT/CT image fusion radiation treatment planning in non-small cell lung cancer(NSCLC) and the impact on V20 and radiation pneumonitis(RP).Methods:Patients ...Objective:The aim of our study was to investigate the value of CT/CT image fusion radiation treatment planning in non-small cell lung cancer(NSCLC) and the impact on V20 and radiation pneumonitis(RP).Methods:Patients who were pathologically or cytologically diagnosed of stage IIIA and IIIB NSCLC were treated with three-dimensional conformal radiation therapy(4000 cGy).Forty patients got at least 25% tumor reduction were randomly divided into two groups:group A of regular shrink field radiotherapy(20 cases) and group B of CT/CT image fused shrink field radiotherapy(20 cases).Dosage reached 6600 cGy.Clinical data,V20 and RP were observed within 3 months after radiotherapy.Statistical analysis was conducted for the NSCLC patients.Results:22.5%(9/40) patients got RP during follow-up.Group A accounted for 6 cases(30%),and group B had 3 cases(15%).There was no marked difference between the two groups(P = 0.256),univariate analysis revealed that the IV20 of A and B groups,and IV20 and CV20 of all patients were statistically related to the incidence of RP(P < 0.05).With Wilcoxon method assay,the ipsilateral lung V20 and contralateral lung V20 had statistical significance between the two groups(P < 0.05).Conclusion:The CT/CT image infusion treatment planning could increase the radical dosage with better tumor control probability but won't increase adverse reaction.展开更多
In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the co...In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.展开更多
<span style="font-family:Verdana;">Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor exte...<span style="font-family:Verdana;">Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.</span>展开更多
Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentatio...Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.展开更多
Eyes are important organs-at-risk （OARs） that should be protected during the radiation treatment of those head tumors. Correct delineation of the eyes on CT images is one of important issues for treatment planning t...Eyes are important organs-at-risk （OARs） that should be protected during the radiation treatment of those head tumors. Correct delineation of the eyes on CT images is one of important issues for treatment planning to protect the eyes as much as possible. In this paper, we propose a new method, named ant colony optimization （ACO）, to delineate the eyes automatically. In the proposed algorithm, each ant tries to find a closed path, and some pheromone is deposited on the visited path when the ant fmds a path. After all ants fmish a circle, the best ant will lay some pheromone to enforce the best path. The proposed algorithm is verified on several CT images, and the preliminary results demonstrate the feasibility of ACO for the delineation problem.展开更多
In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel ...In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.展开更多
In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact ...In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact the hormonal and nutritional balance in the human body.The earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver.The proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for training.In the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation.Then,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts.It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.展开更多
Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models hav...Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its stages.Besides,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response moni-toring.Hence,there is a need exists to develop automated tools for liver cancer detection in a precise manner.With this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)model.The proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation process.Besides,Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver.Moreover,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature vectors.At last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer effectively.In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different measures.The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.展开更多
In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial netw...In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.展开更多
Computed tomography(CT) blurring caused by point spread function leads to errors in quantification and visualization. In this paper, multichannel blind CT image restoration is proposed to overcome the effect of point ...Computed tomography(CT) blurring caused by point spread function leads to errors in quantification and visualization. In this paper, multichannel blind CT image restoration is proposed to overcome the effect of point spread function. The main advantage from multichannel blind CT image restoration is to exploit the diversity and redundancy of information in different acquisitions. The proposed approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is addressed with the alternating direction method of multipliers and simply implemented in the Fourier domain. Numerical experiments illustrate that our method obtains a higher average gain value of at least 1.21 d B in terms of Q metric than the other methods, and it requires only 7 iterations of alternating minimization to obtain a fast convergence.展开更多
In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to in...In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.展开更多
Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our ...Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme.展开更多
In this article, we propose a convolutional neural network (CNN)-based model, a ResNet-50 based model, for discriminating coronavirus disease 2019 (COVID-19) from Non-COVID-19 using chest CT. We adopted the use of wav...In this article, we propose a convolutional neural network (CNN)-based model, a ResNet-50 based model, for discriminating coronavirus disease 2019 (COVID-19) from Non-COVID-19 using chest CT. We adopted the use of wavelet coefficients of the entire image without cropping any parts of the image as input to the CNN model. One of the main contributions of this study is to implement an algorithm called gradient-weighted class activation mapping to produce a heat map for visually verifying where the CNN model is looking at the image, thereby, ensuring the model is performing correctly. In order to verify the effectiveness and usefulness of the proposed method, we compare the obtained results with that obtained by using pixel values of original images as input to the CNN model. The measures used for performance evaluation include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and Matthews correlation coefficient (MCC). The overall classification accuracy, F1 score, and MCC for the proposed method (using wavelet coefficients as input) were 92.2%, 0.915%, and 0.839%, and those for the compared method (using pixel values of the original image as input) were 88.3%, 0.876%, and 0.766%, respectively. The experiment results demonstrate the superiority of the proposed method. Moreover, as a comprehensible classification model, the interpretability of classification results was introduced. The region of interest extracted by the proposed model was visualized using heat maps and the probability score was also shown. We believe that our proposed method could provide a promising computerized toolkit to help radiologists and serve as a second eye for them to classify COVID-19 in CT scan screening examination.展开更多
Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples sub...Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics.A novel double threshold segmentation algorithm is proposed to segment cracks,pores and grains,and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous geomaterials.Moreover,novel relations among these pore scale variables for permeability prediction are established,and the evolution process of cracks is investigated.The results indicate that the porescale permeability is prominently improved by cracks.In addition,excellent agreements are found between the measured and the estimated pore scale variables and permeability.The established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.展开更多
Remote medical diagnosis can be realized by using the Internet,but when transmitting medical images of patients through the Internet,personal information of patients may be leaked.Aim at the security of medical inform...Remote medical diagnosis can be realized by using the Internet,but when transmitting medical images of patients through the Internet,personal information of patients may be leaked.Aim at the security of medical information system and the protection of medical images,a novel robust zero-watermarking based on SIFT-DCT(Scale Invariant Feature Transform-Discrete Cosine Transform)for medical images in the encrypted domain is proposed.Firstly,the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images.Then,the SIFT-DCT is used to extract the feature sequences of encrypted medical images.Next,zero-watermarking technology is used to ensure that the region of interest of medical images are not changed.Finally,the robust of the algorithm is evaluated by the correlation coefficient between the original watermark and the attacked watermark.A series of attack experiments are carried out on this method,and the results show that the algorithm is not only secure,but also robust to both traditional and geometric attacks,especially in clipping attacks.展开更多
Content-based image retrieval has been an active area of research for more than ten years. Gabor schemes and support vector machine （SVM） method have been proven effective in image representation and clas-sification...Content-based image retrieval has been an active area of research for more than ten years. Gabor schemes and support vector machine （SVM） method have been proven effective in image representation and clas-sification. In this paper, we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography （CT） images query. In our experiments, a batch of hepatic CT images containing several types of CT findings are used for the retrieval test. Precision comparison between our scheme and existing methods is presented.展开更多
基金supported in part by the National Natural Science Foundation of China[62301374]Hubei Provincial Natural Science Foundation of China[2022CFB804]+2 种基金Hubei Provincial Education Research Project[B2022057]the Youths Science Foundation of Wuhan Institute of Technology[K202240]the 15th Graduate Education Innovation Fund of Wuhan Institute of Technology[CX2023295].
文摘This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement.However,fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging.To deal with this problem,we propose a 3D multi-scale attention VoxelMorph(MAVoxelMorph)registration network.To alleviate the large deformation problem,a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction,and position-aware axial attention for long-distance dependencies between pixels capture.To further improve the large deformation and fine structure registration results,a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers.Our method was evaluated on four public lung datasets(DIR-Lab dataset,Creatis dataset,Learn2Reg dataset,OASIS dataset)and a local dataset.Results proved that the proposed method achieved better registration performance than current state-of-the-art methods,especially in handling the registration of large deformations and fine structures.It also proved to be fast in 3D image registration,using about 1.5 s,and faster than most methods.Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.
基金the Natural Science Foundation of Zhejiang Province(No.LQ20F020024)。
文摘Various and intricate varieties of lung disease have made it challenging for computer aided diagnosis to appropriately segment lung lesions utilizing computed tomography(CT)images.This study integrates transfer learning with the attention mechanism to construct a deep learning model that can automatically detect new coronary pneumonia on lung CT images.In this study,using VGG16 pre-trained by ImageNet as the encoder,the decoder was established utilizing the U-Net structure.The attention module is incorporated during each concatenate procedure,permitting the model to concentrate on the critical information and identify the crucial components efficiently.The public COVID-19-CT-Seg-Benchmark dataset was utilized for experiments,and the highest scores for Dice,F1,and Accuracy were 0.9071,0.9076,and 0.9965,respectively.The generalization performance was assessed concurrently,with performance metrics including Dice,F1,and Accuracy over 0.8.The experimental findings indicate the feasibility of the segmentation network proposed in this study.
基金supported by the National Natural and Science Foundation of China under Grant No.12301662Zhejiang Provincial Natural Science Foundation of China under Grant No.LQ21F030019.
文摘Automatic pancreas segmentation in CT scans is crucial for various medical applications including early disease detection,treatment planning and therapeutic evaluation.However,the pancreas’s small size,irregular morphology,and low contrast with surrounding tissues make accurate pancreas segmentation still a challenging task.To address these challenges,we propose a novel RPMS-DSAUnet for accurate automatic pancreas segmentation in abdominal CT images.First,a Residual Pyramid Squeeze Attention module enabling hierarchical multi-resolution feature extraction with dynamic feature weighting and selective feature reinforcement capabilities is integrated into the backbone network,enhancing pancreatic feature extraction and improving localization accuracy.Second,a Multi-Scale Feature Extraction module is embedded into the network to expand the receptive field while preserving feature map resolution,mitigate feature degradation caused by network depth,and maintain awareness of pancreatic anatomical structures.Third,a Dimensional Squeeze Attention module is designed to reduce interference from adjacent organs and highlight useful pancreatic features through spatial-channel interaction,thereby enhancing sensitivity to small targets.Finally,a hybrid loss function combining Dice loss and Focal loss is employed to alleviate class imbalance issues.Extensive evaluation on two public datasets(NIH and MSD)shows that the proposed RPMS-DSAUnet achieves Dice Similarity Coefficients of 85.51%and 80.91%,with corresponding Intersection over Union(IoU)scores of 74.93%and 67.94%on each dataset,respectively.Experimental results demonstrate superior performance of the proposed model over baseline methods and state-of-the-art approaches,validating its effectiveness for CT-based pancreas segmentation.
基金supported by the National Key Research and Development Program of China(No.2016YFC1100600)the National Nature Science Foundation of China(Nos.61540006,61672363).
文摘We present a method for computed tomography(CT)image processing and modeling for tibia microstructure,achieved by using computer graphics and fractal theory.Given the large-scale image data of tibia species with DICOM standard for clinical applications,we take advantage of algorithms such as image binarization,hot pixel removing and close operation to obtain visually clear image for tibia microstructure.All of these images are based on 20 CT scanning images with 30μm slice thickness and 30μm interval and continuous changes in pores.For each pore,we determine its profile by using an improved algorithm for edge detection.Then,to calculate its three-dimensional fractal dimension,we measure the circumference perimeter and area of the pores of bone microstructure using a line fitting method based on the least squares.Subsequently,we put forward an algorithm for the pore profiles through ellipse fitting.The results show that the pores have significant fractal characteristics because of the good linear correlation between the perimeter and the area parameters in log–log scale coordinates system,and the ratio of the elliptical short axis to the long axis through ellipse fitting tends to 0.6501.Based on support vector machine and structural risk minimization principle,we put forward a mapping database theory of structure parameters among the pores of CT images and fractal dimension,Poisson’s ratios,porosity and equivalent aperture.On this basis,we put forward a new concept for 3D modeling called precision-measuring digital expressing to reconstruct tibia microstructure for human hard tissue.
文摘Objective:The aim of our study was to investigate the value of CT/CT image fusion radiation treatment planning in non-small cell lung cancer(NSCLC) and the impact on V20 and radiation pneumonitis(RP).Methods:Patients who were pathologically or cytologically diagnosed of stage IIIA and IIIB NSCLC were treated with three-dimensional conformal radiation therapy(4000 cGy).Forty patients got at least 25% tumor reduction were randomly divided into two groups:group A of regular shrink field radiotherapy(20 cases) and group B of CT/CT image fused shrink field radiotherapy(20 cases).Dosage reached 6600 cGy.Clinical data,V20 and RP were observed within 3 months after radiotherapy.Statistical analysis was conducted for the NSCLC patients.Results:22.5%(9/40) patients got RP during follow-up.Group A accounted for 6 cases(30%),and group B had 3 cases(15%).There was no marked difference between the two groups(P = 0.256),univariate analysis revealed that the IV20 of A and B groups,and IV20 and CV20 of all patients were statistically related to the incidence of RP(P < 0.05).With Wilcoxon method assay,the ipsilateral lung V20 and contralateral lung V20 had statistical significance between the two groups(P < 0.05).Conclusion:The CT/CT image infusion treatment planning could increase the radical dosage with better tumor control probability but won't increase adverse reaction.
基金supported by National Natural Science Foundation ofChina (61672279)Project of “Six Talents Peak” in Jiangsu (2012-WLW-023)OpenFoundation of State Key Laboratory of Hydrology-Water Resources and HydraulicEngineering, Nanjing Hydraulic Research Institute, China (2016491411).
文摘In order to improve the quality of low-dose computational tomography (CT)images, the paper proposes an improved image denoising approach based on WGAN-gpwith Wasserstein distance. For improving the training and the convergence efficiency, thegiven method introduces the gradient penalty term to WGAN network. The novelperceptual loss is introduced to make the texture information of the low-dose imagessensitive to the diagnostician eye. The experimental results show that compared with thestate-of-art methods, the time complexity is reduced, and the visual quality of low-doseCT images is significantly improved.
文摘<span style="font-family:Verdana;">Rationale and Objectives: Accurately establishing the diagnosis and staging of cervical and thyroid cancers is essential in medical practice in determining tumor extension and dissemination and involves the most accurate and effective therapeutic approach. For accurate diagnosis and staging of cervical and thyroid cancers, we aim to create a diagnostic method, optimized by the algorithms of artificial intelligence and validated by achieving accurate and favorable results by conducting a clinical trial, during which we will use the diagnostic method optimized by artificial intelligence (AI) algorithms, to avoid errors, to increase the understanding on interpretation computer tomography (CT) scan, magnetic resonance imaging (MRI) of the doctor and improve therapeutic planning. Materials and Methods: The optimization of the computer assisted diagnosis (CAD) method will consist in the development and formation of artificial intelligence models, using algorithms and tools used in segmental volumetric constructions to generate 3D images from MRI/CT. We propose a comparative study of current developments in “DICOM” image processing by volume rendering technique, the use of the transfer function for opacity and color, shades of gray from “DICOM” images projected in a three-dimensional space. We also use artificial intelligence (AI), through the technique of Generative Adversarial Networks (GAN), which has proven to be effective in representing complex data distributions, as we do in this study. Validation of the diagnostic method, optimized by algorithm of artificial intelligence will consist of achieving accurate results on diagnosis and staging of cervical and thyroid cancers by conducting a randomized, controlled clinical trial, for a period of 17 months. Results: We will validate the CAD method through a clinical study and, secondly, we use various network topologies specified above, which have produced promising results in the tasks of image model recognition and by using this mixture. By using this method in medical practice, we aim to avoid errors, provide precision in diagnosing, staging and establishing the therapeutic plan in cancers of the cervix and thyroid using AI. Conclusion: The use of the CAD method can increase the quality of life by avoiding intra and postoperative complications in surgery, intraoperative orientation and the precise determination of radiation doses and irradiation zone in radiotherapy.</span>
基金Science Special Fund for "Special Training" of Ethnical Minority Professional and Technical Intelligent in Xinjiang sponsored by the Scienceand Technology Department of Xinjiang Uygur Autonomous Regiongrant number:200723104+1 种基金National Natural Science Foundation of Chinagrant number:30960097
文摘Liver hydatid disease is a common parasitic disease in farm and pastoral areas, which seriously influences people's health. Based on CT imaging features of this disease, an iterative approach for liver segmentation and hydatid lesion extraction simultaneously is proposed. In each iteration, our algorithm consists of two main steps: 1) according to the user-defined pixel seeds in the liver and hydatid lesion, Gaussian probability model fitting and smoothed Bayesian classification are applied to get initial segmentation of liver and lesion; 2) the parametric active contour model using priori shape force field is adopted to refine initial segmentation. We make subjective and objective evaluation on the proposed algorithm validity by the experiments of liver and hydatid lesion segmentation on different patients' CT slices. In comparison with ground-truth manual segmentation results, the experimental results show the effectiveness of our method to segment liver and hydatid lesion.
基金Supported by the 973 Project of China (No. 2003CB716106)the National Natural Science Foundation of China (No. 30500140 and 90208003)
文摘Eyes are important organs-at-risk （OARs） that should be protected during the radiation treatment of those head tumors. Correct delineation of the eyes on CT images is one of important issues for treatment planning to protect the eyes as much as possible. In this paper, we propose a new method, named ant colony optimization （ACO）, to delineate the eyes automatically. In the proposed algorithm, each ant tries to find a closed path, and some pheromone is deposited on the visited path when the ant fmds a path. After all ants fmish a circle, the best ant will lay some pheromone to enforce the best path. The proposed algorithm is verified on several CT images, and the preliminary results demonstrate the feasibility of ACO for the delineation problem.
文摘In order to effectively improve the pathological diagnosis capability and feature resolution of 3D human brain CT images,a threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is proposed in this paper.In this method,first,original 3D human brain image information is collected,and CT image filtering is performed to the collected information through the gradient value decomposition method,and edge contour features of the 3D human brain CT image are extracted.Then,the threshold segmentation method is adopted to segment the regional pixel feature block of the 3D human brain CT image to segment the image into block vectors with high-resolution feature points,and the 3D human brain CT image is reconstructed with the salient feature point as center.Simulation results show that the method proposed in this paper can provide accuracy up to 100%when the signal-to-noise ratio is 0,and with the increase of signal-to-noise ratio,the accuracy provided by this method is stable at 100%.Comparison results show that the threshold segmentation method of multi-resolution 3D human brain CT image based on edge pixel grayscale feature decomposition is signicantly better than traditional methods in pathological feature estimation accuracy,and it effectively improves the rapid pathological diagnosis and positioning recognition abilities to CT images.
基金The authors have not received any specific funding for this study.This pursuit is a part of their scholarly endeavors.
文摘In the digestion of amino acids,carbohydrates,and lipids,as well as protein synthesis from the consumed food,the liver has many diverse responsibilities and functions that are to be performed.Liver disease may impact the hormonal and nutritional balance in the human body.The earlier diagnosis of such critical conditions may help to treat the patient effectively.A computationally efficient AW-HARIS algorithm is used in this paper to perform automated segmentation of CT scan images to identify abnormalities in the human liver.The proposed approach can recognize the abnormalities with better accuracy without training,unlike in supervisory procedures requiring considerable computational efforts for training.In the earlier stages,the CT images are pre-processed through an Adaptive Multiscale Data Condensation Kernel to normalize the underlying noise and enhance the image’s contrast for better segmentation.Then,the preliminary phase’s outcome is being fed as the input for the Anisotropic Weighted—Heuristic Algorithm for Real-time Image Segmentation algorithm that uses texture-related information,which has resulted in precise outcome with acceptable computational latency when compared to that of its counterparts.It is observed that the proposed approach has outperformed in the majority of the cases with an accuracy of 78%.The smart diagnosis approach would help the medical staff accurately predict the abnormality and disease progression in earlier ailment stages.
基金The Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,Saudi Arabia has funded this project,under grant no.(FP-206-43).
文摘Liver cancer is one of the major diseases with increased mortality in recent years,across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis(CAD)models have been developed to detect the presence of liver cancer accurately and classify its stages.Besides,liver cancer segmentation outcome,using medical images,is employed in the assessment of tumor volume,further treatment plans,and response moni-toring.Hence,there is a need exists to develop automated tools for liver cancer detection in a precise manner.With this motivation,the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification(IAIEO-LCC)model.The proposed IAIEO-LCC technique initially performs Median Filtering(MF)-based pre-processing and data augmentation process.Besides,Kapur’s entropy-based segmentation technique is used to identify the affected regions in liver.Moreover,VGG-19 based feature extractor and Equilibrium Optimizer(EO)-based hyperparameter tuning processes are also involved to derive the feature vectors.At last,Stacked Gated Recurrent Unit(SGRU)classifier is exploited to detect and classify the liver cancer effectively.In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance,a wide range of simulations was conducted and the results were inspected under different measures.The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.
基金supported by National Natural Science Foundation of China(No.11802272)China Postdoctoral Science Foundation(No.2019M651085)。
文摘In order to solve the problems of artifacts and noise in low-dose computed tomography(CT)images in clinical medical diagnosis,an improved image denoising algorithm under the architecture of generative adversarial network(GAN)was proposed.First,a noise model based on style GAN2 was constructed to estimate the real noise distribution,and the noise information similar to the real noise distribution was generated as the experimental noise data set.Then,a network model with encoder-decoder architecture as the core based on GAN idea was constructed,and the network model was trained with the generated noise data set until it reached the optimal value.Finally,the noise and artifacts in low-dose CT images could be removed by inputting low-dose CT images into the denoising network.The experimental results showed that the constructed network model based on GAN architecture improved the utilization rate of noise feature information and the stability of network training,removed image noise and artifacts,and reconstructed image with rich texture and realistic visual effect.
基金Supported by the National Natural Science Foundaton of China(No.61340034)China Postdoctoral Science Foundation(No.2013M530873)the Research Program of Application Foundation and Advanced Technology of Tianjin(No.13JCYBJC15600)
文摘Computed tomography(CT) blurring caused by point spread function leads to errors in quantification and visualization. In this paper, multichannel blind CT image restoration is proposed to overcome the effect of point spread function. The main advantage from multichannel blind CT image restoration is to exploit the diversity and redundancy of information in different acquisitions. The proposed approach is based on a variable splitting to obtain an equivalent constrained optimization formulation, which is addressed with the alternating direction method of multipliers and simply implemented in the Fourier domain. Numerical experiments illustrate that our method obtains a higher average gain value of at least 1.21 d B in terms of Q metric than the other methods, and it requires only 7 iterations of alternating minimization to obtain a fast convergence.
文摘In civil aviation security screening, laptops, with their intricate structural composition, provide the potential for criminals to conceal dangerous items. Presently, the security process necessitates passengers to individually present their laptops for inspection. The paper introduced a method for laptop removal. By combining projection algorithms with the YOLOv7-Seg model, a laptop’s three views were generated through projection, and instance segmentation of these views was achieved using YOLOv7-Seg. The resulting 2D masks from instance segmentation at different angles were employed to reconstruct a 3D mask through angle restoration. Ultimately, the intersection of this 3D mask with the original 3D data enabled the successful extraction of the laptop’s 3D information. Experimental results demonstrated that the fusion of projection and instance segmentation facilitated the automatic removal of laptops from CT data. Moreover, higher instance segmentation model accuracy leads to more precise removal outcomes. By implementing the laptop removal functionality, the civil aviation security screening process becomes more efficient and convenient. Passengers will no longer be required to individually handle their laptops, effectively enhancing the efficiency and accuracy of security screening.
基金the National Natural Science Foundation of China (No. 30770589)
文摘Hepatic computed tomography(CT) images with Gabor function were analyzed.Then a threshold-based classification scheme was proposed using Gabor features and proceeded with the retrieval of the hepatic CT images.In our experiments, a batch of hepatic CT images containing several types of CT findings was used and compared with the Zhao's image classification scheme, support vector machines(SVM) scheme and threshold-based scheme.
文摘In this article, we propose a convolutional neural network (CNN)-based model, a ResNet-50 based model, for discriminating coronavirus disease 2019 (COVID-19) from Non-COVID-19 using chest CT. We adopted the use of wavelet coefficients of the entire image without cropping any parts of the image as input to the CNN model. One of the main contributions of this study is to implement an algorithm called gradient-weighted class activation mapping to produce a heat map for visually verifying where the CNN model is looking at the image, thereby, ensuring the model is performing correctly. In order to verify the effectiveness and usefulness of the proposed method, we compare the obtained results with that obtained by using pixel values of original images as input to the CNN model. The measures used for performance evaluation include accuracy, sensitivity, specificity, positive predictive value, negative predictive value, F1 score, and Matthews correlation coefficient (MCC). The overall classification accuracy, F1 score, and MCC for the proposed method (using wavelet coefficients as input) were 92.2%, 0.915%, and 0.839%, and those for the compared method (using pixel values of the original image as input) were 88.3%, 0.876%, and 0.766%, respectively. The experiment results demonstrate the superiority of the proposed method. Moreover, as a comprehensible classification model, the interpretability of classification results was introduced. The region of interest extracted by the proposed model was visualized using heat maps and the probability score was also shown. We believe that our proposed method could provide a promising computerized toolkit to help radiologists and serve as a second eye for them to classify COVID-19 in CT scan screening examination.
基金supported by the National Natural Science Foundation of China(Grant Nos.51839009 and 51679017)the Graduate Research and Innovation Foundation of Chongqing,China(Grant No.CYB18037).
文摘Pore scale variables(e.g.,porosity,grain size)are important indexes to predict the hydraulic properties of porous geomaterials.X-ray images from ten types of intact sandstones and another type of sandstone samples subjected to triaxial compression are used to investigate the permeability and fracture characteristics.A novel double threshold segmentation algorithm is proposed to segment cracks,pores and grains,and pore scale variables are defined and extracted from these X-ray CT images to study the geometric characteristics of microstructures of porous geomaterials.Moreover,novel relations among these pore scale variables for permeability prediction are established,and the evolution process of cracks is investigated.The results indicate that the porescale permeability is prominently improved by cracks.In addition,excellent agreements are found between the measured and the estimated pore scale variables and permeability.The established correlations can be employed to effectively identify the hydraulic properties of porous geomaterials.
基金This work is supported by the Key Reach Project of Hainan Province[ZDYF2018129]the National Natural Science Foundation of China[61762033]+3 种基金the National Natural Science Foundation of Hainan[2018CXTD333]the Key Innovation and Entrepreneurship Project of Hainan University[Hdcxcyxm201711]the Higher Education Research Project of Hainan Province(Hnky2019-73)the Key Research Project of Haikou College of Economics[HJKZ18-01].
文摘Remote medical diagnosis can be realized by using the Internet,but when transmitting medical images of patients through the Internet,personal information of patients may be leaked.Aim at the security of medical information system and the protection of medical images,a novel robust zero-watermarking based on SIFT-DCT(Scale Invariant Feature Transform-Discrete Cosine Transform)for medical images in the encrypted domain is proposed.Firstly,the original medical image is encrypted in transform domain based on Logistic chaotic sequence to enhance the concealment of original medical images.Then,the SIFT-DCT is used to extract the feature sequences of encrypted medical images.Next,zero-watermarking technology is used to ensure that the region of interest of medical images are not changed.Finally,the robust of the algorithm is evaluated by the correlation coefficient between the original watermark and the attacked watermark.A series of attack experiments are carried out on this method,and the results show that the algorithm is not only secure,but also robust to both traditional and geometric attacks,especially in clipping attacks.
基金the Joint National Natural Science Foundation of China under Grant No.30770589.
文摘Content-based image retrieval has been an active area of research for more than ten years. Gabor schemes and support vector machine （SVM） method have been proven effective in image representation and clas-sification. In this paper, we propose a retrieval scheme based on Gabor filters and SVMs for hepatic computed tomography （CT） images query. In our experiments, a batch of hepatic CT images containing several types of CT findings are used for the retrieval test. Precision comparison between our scheme and existing methods is presented.
