Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins ar...Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.展开更多
Imaging evaluation of lymph node metastasis and infiltration faces problems such as low artificial outline efficiency and insufficient consistency.Deep learning technology based on convolutional neural networks has gr...Imaging evaluation of lymph node metastasis and infiltration faces problems such as low artificial outline efficiency and insufficient consistency.Deep learning technology based on convolutional neural networks has greatly improved the technical effect of radiomics in lymph node pathological characteristics analysis and efficacy monitoring through automatic lymph node detection,precise segmentation and three-dimensional reconstruction algorithms.This review focuses on the automatic lymph node segmentation model,treatment response prediction algorithm and benign and malignant differential diagnosis system for multimodal imaging,in order to provide a basis for further research on artificial intelligence to assist lymph node disease management and clinical decision-making,and provide a reference for promoting the construction of a system for accurate diagnosis,personalized treatment and prognostic evaluation of lymph node-related diseases.展开更多
Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great signi...Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface.A multibeam echo-sounder system(MBES)can record the complete backscatter intensity of the water column,and it is one of the most effective means for detecting cold seeps.However,the gas plumes recorded in multibeam water column images(WCI)are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES,making it difficult to obtain the effective segmentation.Therefore,based on the existing UNet semantic segmentation network,this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes.Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods.The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference.The segmentation precision,the Dice coefficient,and the recall rate of this model are 92.09%,92.00%,and 92.49%,respectively,which are 1.17%,2.10%,and 2.07%higher than the results of the UNet.展开更多
In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ...In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.展开更多
Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical i...Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.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
Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical ins...Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics,especially for supersonic inlet whose performance is highly associated with that of the whole flight.However,conventional shock wave identification methods have limited accuracy in segmenting the shock wave.To overcome the limitation,we proposed an automated shock wave identification method(SW-Segment)that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search.We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image.The results proved that SW-Segment showed a shock wave identification accuracy of 95.24%in the numerical schlieren image and an accuracy of 88.33%in the experimental image,clearly demonstrating its reliability.SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios,offering robust data support for flow field analysis and supersonic flight design.展开更多
The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manu...The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manual detection of lesions is very time consuming and lacks accuracy.Most of the lesions are difficult to detect manually,especially within the grey matter.This paper proposes a novel and fully automated convolution neural network(CNN)approach to segment lesions.The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly.The first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase efficiency.The system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with CNN.Three routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI artifacts.We evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and ISBI.Quantitative and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art methods.The proposed method shows consistent higher precision and sensitivity than other methods.The proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.展开更多
3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with m...3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.展开更多
Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic Analysis （LSA） is first used to extract semantic knowledge from a given document, all questio...Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic Analysis （LSA） is first used to extract semantic knowledge from a given document, all question paragraphs are identified, an automatic text segmentation approach analogous to Text＇filing is exploited to improve the precision of correlating question paragraphs and answer paragraphs, and finally some ＂important＂ sentences are extracted from the generic content and the question-answer pairs to generate a complete summary. Experimental results showed that our approach is highly efficient and improves significantly the coherence of the summary while not compromising informativeness.展开更多
AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures we...AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.展开更多
Numerous methods have been published to segment the infarct tissue in theleft ventricle, most of them either need manual work, post-processing, or suffer from poorreproducibility. We proposed an automatic segmentation...Numerous methods have been published to segment the infarct tissue in theleft ventricle, most of them either need manual work, post-processing, or suffer from poorreproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue irleft ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 humanhearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundariesof the ventricles in every 2D slice of the cardiac magnetic resonance with late gadoliniumenhancement images were manually segmented. The subsequent pipeline of infarct tissuesegmentation is fully automatic. The segmentation results with the automatic algorithm proposed inthis paper were compared to the consensus ground truth. The median of Dice overlap between ourautomatic method and the consensus ground truth is 0.79. We also compared the automatic methodwith the consensus ground truth using different image sources from diferent centers with diferentscan parameters and different scan machines. The results showed that the Dice overlap with thepublic dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method isrobust with respect to different MRI image sources, which were scanned by different centers withdifferent image collection parameters. The segmentation accuracy we obtained is comparable toor better than that of the conventional semi-automatic methods. Our segmentation method may beuseful for processing large amount of dataset in clinic.展开更多
Segmentation of layers in retinal images obtained by optical coherence tomography(OCT)has become an important clinical tool to diagnose ophthalmic diseases.However,due to the sus-ceptibility to speckle noise and shado...Segmentation of layers in retinal images obtained by optical coherence tomography(OCT)has become an important clinical tool to diagnose ophthalmic diseases.However,due to the sus-ceptibility to speckle noise and shadow of blood vessels etc.,the layer segmentation technology based on a single image still fail to reach a satisfactory level.We propose a combination method of structure interpolation and lateral mean filtering(SI-LMF)to improve the signal-to-noise ratio based on one retinal image.Before performing one-dimensional lateral mean filtering to remove noise,structure interpolation was operated to eliminate thickness fluctuations.Then,we used boundary growth method to identify boundaries.Compared with existing segmentations,the method proposed in this paper requires less data and avoids the influence of microsaccade.The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects,which successfully identified the boundaries of 10 physio-logical layers,consistent with the results based on the manual determination.展开更多
Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scori...Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scoring R-CNN.First,an attention mechanism and a feature fusion module are introduced,to improve feature representation.Second,a new classifier head—consisting of four convolutional layers and a fully connected layer—is proposed,to reduce the influence of information around the area of the defect.Third,to evaluate the proposed method,a dataset of aircraft skin defects was constructed,containing 276 images with a resolution of 960×720 pixels.Experimental results show that the proposed classifier head improves the detection and segmentation accuracy,for aircraft skin defect inspection,more effectively than the attention mechanism and feature fusion module.Compared with the Mask R-CNN and Mask Scoring R-CNN,the proposed method increased the segmentation precision by approximately 21%and 19.59%,respectively.These results demonstrate that the proposed method performs favorably against the other two methods of pixellevel aircraft skin defect detection.展开更多
Microwave technology offers the possibility for pre-hospital stroke detection as we have previously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technic...Microwave technology offers the possibility for pre-hospital stroke detection as we have previously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diagnostics. A priori information can be obtained by segmenting the patient’s head tissues from magnetic resonance images. Expert manual segmentation is presently the gold standard, but it is laborious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real data from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accuracy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accuracy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intracerebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intracerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmentation can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system.展开更多
In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft grip...In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft gripper structures and a soft bionic bracket structure.We adopt the local thin-walled design in the soft gripper structures.This design improves the grippers’bending efficiency,and imitate human finger’s segmental bending function.In addition,this work also proposes a pneumatic soft bionic bracket structure,which not only can fix grippers,but also can automatically adjust the grasping space by imitating the human adjacent fingers’opening and closing movements.Due to the above advantages,the SBGD can grasp larger or smaller objects than the regular grasping devices.Particularly,to grasp small objects reliably,we further present a new Pinching Grasping(PG)method.The great performance of the fully SBGD is verified by experiments.This work will promote innovative development of the soft bionic grasping robots,and greatly meet the applications of dexterous grasping multi-size and multi-shape objects.展开更多
Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,...Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.展开更多
An accurate segmentation and quantification of the superficial foveal avascular zone(sFAZ)is important to facilitate the diagnosis and treatment of many retinal diseases,such as diabetic retinopathy and retinal vein o...An accurate segmentation and quantification of the superficial foveal avascular zone(sFAZ)is important to facilitate the diagnosis and treatment of many retinal diseases,such as diabetic retinopathy and retinal vein occlusion.We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography(OCTA)images with robustness to brightness and contrast(B/C)variations.A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth(GT)was manually segmented subsequently.A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class.Subsequently,we applied largestconnected-region extraction and hole-filling to fine-tune the automatic segmentation results.A maximum mean dice similarity coefficient(DSC)of 0.976±0.011 was obtained when the automatic segmentation results were compared against the GT.The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997.In all nine parameter groups with various brightness/contrast,all the DSCs of the proposed method were higher than 0.96.The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods.In conclusion,we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations.For clinical applications,this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.展开更多
The internal quality inspection of the continuous casting billets is very important,and mis-inspection will seriously affect the subsequent production process.The UNet-VGG16 transfer learning model was used for semant...The internal quality inspection of the continuous casting billets is very important,and mis-inspection will seriously affect the subsequent production process.The UNet-VGG16 transfer learning model was used for semantic segmentation of the central shrinkage defects of the continuous casting billets.The automatic recognition accuracy of the central shrinkage defects of the continuous casting billets reaches more than 0.9.We use the minimum circumscribed rectangle to quantify the geometric dimensions such as length,width and area of the central shrinkage defects and use the threshold method to rate the central shrinkage defects of the continuous casting billets.The results show that all the testing images are rated correctly,and this method achieves the automatic recognition and intelligent analysis of the central shrinkage defects of the continuous casting billets.展开更多
Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Cu...Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.展开更多
基金supported by the Innovation Foundation of National Commercial Aircraft Manufacturing Engineering Technology Research Center(No.COMAC-SFGS-2022-1877)in part by the National Natural Science Foundation of China(No.92048301)。
文摘Cabin cables,as critical components of an aircraft's electrical system,significantly impact the operational efficiency and safety of the aircraft.The existing cable segmentation methods in civil aviation cabins are limited,especially in automation,heavily dependent on large amounts of data and resources,lacking the flexibility to adapt to different scenarios.To address these challenges,this paper introduces a novel image segmentation model,CableSAM,specifically designed for automated segmentation of cabin cables.CableSAM improves segmentation efficiency and accuracy using knowledge distillation and employs a context ensemble strategy.It accurately segments cables in various scenarios with minimal input prompts.Comparative experiments on three cable datasets demonstrate that CableSAM surpasses other advanced cable segmentation methods in performance.
基金Supported by Clinical Trials from the Nanjing Drum Tower Hospital,Affiliated Hospital of Medical School,Nanjing University,No.2021-LCYJ-MS-11Nanjing Drum Tower Hospital National Natural Science Foundation Youth Cultivation Project,No.2024-JCYJQP-15.
文摘Imaging evaluation of lymph node metastasis and infiltration faces problems such as low artificial outline efficiency and insufficient consistency.Deep learning technology based on convolutional neural networks has greatly improved the technical effect of radiomics in lymph node pathological characteristics analysis and efficacy monitoring through automatic lymph node detection,precise segmentation and three-dimensional reconstruction algorithms.This review focuses on the automatic lymph node segmentation model,treatment response prediction algorithm and benign and malignant differential diagnosis system for multimodal imaging,in order to provide a basis for further research on artificial intelligence to assist lymph node disease management and clinical decision-making,and provide a reference for promoting the construction of a system for accurate diagnosis,personalized treatment and prognostic evaluation of lymph node-related diseases.
基金Supported by the National Natural Science Foundation of China (Nos.41930535,41906165)the High-level Foreign Expert Introduction Program (No.G2021025006L)the SDUST Research Fund (No.2019TDJH103)。
文摘Cold seeps are widely developed on the seabed of continental margins and can form gas plumes due to the upward migration of methane-rich fluids.The detection and automatic segmentation of gas plumes are of great significance in locating and studying the cold seep system that is usually accompanied by hydrate layers in the subsurface.A multibeam echo-sounder system(MBES)can record the complete backscatter intensity of the water column,and it is one of the most effective means for detecting cold seeps.However,the gas plumes recorded in multibeam water column images(WCI)are usually blurred due to the interference of the complicated water environment and the sidelobes of the MBES,making it difficult to obtain the effective segmentation.Therefore,based on the existing UNet semantic segmentation network,this paper proposes an AP-UNet network combining the convolutional block attention module and the pyramid pooling module for the automatic segmentation and extraction of gas plumes.Comparative experiments are conducted among three traditional segmentation methods and two deep learning methods.The results show that the AP-UNet segmentation model can effectively suppress complicated water column noise interference.The segmentation precision,the Dice coefficient,and the recall rate of this model are 92.09%,92.00%,and 92.49%,respectively,which are 1.17%,2.10%,and 2.07%higher than the results of the UNet.
文摘In this paper a kind of ECG signal automatic segmentation algorithm based on ECG fractal dimension trajectory is put forward.First,the ECG signal will be analyzed,then constructing the fractal dimension trajectory of ECG signal according to the fractal dimension trajectory constructing algorithm,finally,obtaining ECG signal feature points and ECG automatic segmentation will be realized by the feature of ECG signal fractal dimension trajectory and the feature of ECG frequency domain characteristics.Through Matlab simulation of the algorithm,the results showed that by constructing the ECG fractal dimension trajectory enables ECG location of each component displayed clearly and obtains high success rate of sub-ECG,providing a basis to identify the various components of ECG signal accurately.
基金supported by the National Natural Science Foundation of China(Nos.61502537 and 61573380)the Hunan Provincial Natural Science Foundation of China(Nos.2018JJ3681 and 2016JJ2150)+3 种基金the Open Project Fund of Key Lab of Digital Signal and Image Processing of Guangdong Province(No.2018GDDSIPL-01)the Mutual Creation Project for Teachers and Students(No.2018gczd022)the 111 Project(No.B18059)the Fundamental Research Funds for the Central Universities of Central South University(No.2018zzts576)
文摘Glaucoma as an irreversible blinding opioid neuropathy disease, its blindness rate is the second only after cataract in the world. The optic cup-to-disc ratio(CDR) is generally considered to be an important clinical indicator for judging the severity of glaucoma by ophthalmologists from retinal fundus image. In this letter, we propose an automatic CDR measurement method that consists of a novel optic disc localization method and a simultaneous optic disc and cup segmentation network based on the improved U shape deep convolutional neural network. Experimental results demonstrate that the proposed method can achieve superior performance when compared with other existing methods. Thus, our method can be used as a powerful tool for glaucoma-assisted diagnosis.
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.
基金supported by the National Natural Science Foundation of China(Grant Nos.12402336,U20A2070,12025202)the Natural Science Foundation of Jiangsu Province(Grant No.BK20230876)+2 种基金the National High-Level Talent Project(Grant No.YQR23069)the Key Laboratory of Intake and Exhaust Technology,Ministry of Education(Grant No.CEPE2024015)the Key Laboratory of Mechanics and Control for Aerospace Structures(Nanjing University of Aeronautics and Astronautics)(Grant No.MCAS-I-0325K01)。
文摘Schlieren imaging is a widely used technique to visualize the structure of supersonic flow field,which is usually dominated by shock waves.Precise identification of shock waves in schlieren image provides critical insights for flow diagnostics,especially for supersonic inlet whose performance is highly associated with that of the whole flight.However,conventional shock wave identification methods have limited accuracy in segmenting the shock wave.To overcome the limitation,we proposed an automated shock wave identification method(SW-Segment)that can attain high resolution and automatic shock wave segmentation by integrating correlation-based feature extraction with graph search.We demonstrated the efficacy of SW-Segment via the identification of shock waves in simulatively and experimentally obtained schlieren image.The results proved that SW-Segment showed a shock wave identification accuracy of 95.24%in the numerical schlieren image and an accuracy of 88.33%in the experimental image,clearly demonstrating its reliability.SW-Segment holds broad applicability for shock wave detection in diverse schlieren imaging scenarios,offering robust data support for flow field analysis and supersonic flight design.
基金Thanks to research training program(RTP)of University of Newcastle,Australia and PGRSS,UON for providing funding.APC of CMC will be paid by PGRSS,UON funding.
文摘The diagnosis of multiple sclerosis(MS)is based on accurate detection of lesions on magnetic resonance imaging(MRI)which also provides ongoing essential information about the progression and status of the disease.Manual detection of lesions is very time consuming and lacks accuracy.Most of the lesions are difficult to detect manually,especially within the grey matter.This paper proposes a novel and fully automated convolution neural network(CNN)approach to segment lesions.The proposed system consists of two 2D patchwise CNNs which can segment lesions more accurately and robustly.The first CNN network is implemented to segment lesions accurately,and the second network aims to reduce the false positives to increase efficiency.The system consists of two parallel convolutional pathways,where one pathway is concatenated to the second and at the end,the fully connected layer is replaced with CNN.Three routine MRI sequences T1-w,T2-w and FLAIR are used as input to the CNN,where FLAIR is used for segmentation because most lesions on MRI appear as bright regions and T1-w&T2-w are used to reduce MRI artifacts.We evaluated the proposed system on two challenge datasets that are publicly available from MICCAI and ISBI.Quantitative and qualitative evaluation has been performed with various metrics like false positive rate(FPR),true positive rate(TPR)and dice similarities,and were compared to current state-of-the-art methods.The proposed method shows consistent higher precision and sensitivity than other methods.The proposed method can accurately and robustly segment MS lesions from images produced by different MRI scanners,with a precision up to 90%.
基金supported by the National Natural Science Foundation of China(Grant Nos.52304139,52325403)the CCTEG Coal Mining Research Institute funding(Grant No.KCYJY-2024-MS-10).
文摘3D laser scanning technology is widely used in underground openings for high-precision,rapid,and nondestructive structural evaluations.Segmenting large 3D point cloud datasets,particularly in coal mine roadways with multi-scale targets,remains challenging.This paper proposes an enhanced segmentation method integrating improved PointNet++with a coverage-voted strategy.The coverage-voted strategy reduces data while preserving multi-scale target topology.The segmentation is achieved using an enhanced PointNet++algorithm with a normalization preprocessing head,resulting in a 94%accuracy for common supporting components.Ablation experiments show that the preprocessing head and coverage strategies increase segmentation accuracy by 20%and 2%,respectively,and improve Intersection over Union(IoU)for bearing plate segmentation by 58%and 20%.The accuracy of the current pretraining segmentation model may be affected by variations in surface support components,but it can be readily enhanced through re-optimization with additional labeled point cloud data.This proposed method,combined with a previously developed machine learning model that links rock bolt load and the deformation field of its bearing plate,provides a robust technique for simultaneously measuring the load of multiple rock bolts in a single laser scan.
基金Project (No. 2002AA119050) supported by the National Hi-TechResearch and Development Program (863) of China
文摘Automatic Chinese text summarization for dialogue style is a relatively new research area. In this paper, Latent Semantic Analysis （LSA） is first used to extract semantic knowledge from a given document, all question paragraphs are identified, an automatic text segmentation approach analogous to Text＇filing is exploited to improve the precision of correlating question paragraphs and answer paragraphs, and finally some ＂important＂ sentences are extracted from the generic content and the question-answer pairs to generate a complete summary. Experimental results showed that our approach is highly efficient and improves significantly the coherence of the summary while not compromising informativeness.
文摘AIM:To develop an automated model for subfoveal choroidal thickness(SFCT)detection in optical coherence tomography(OCT)images,addressing manual fovea location and choroidal contour challenges.METHODS:Two procedures were proposed:defining the fovea and segmenting the choroid.Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction(LocBscan-3D)predicted fovea location using central foveal depression features,and fovea localization from two-dimensional en-face OCT(LocEN-2D)used a mask region-based convolutional neural network(Mask R-CNN)model for optic disc detection,and determined the fovea location based on optic disc relative position.Choroid segmentation also employed Mask R-CNN.RESULTS:For 53 eyes in 28 healthy subjects,LocBscan-3D’s mean difference between manual and predicted fovea locations was 170.0μm,LocEN-2D yielded 675.9μm.LocEN-2D performed better in non-high myopia group(P=0.02).SFCT measurements from Mask R-CNN aligned with manual values.CONCLUSION:Our models accurately predict SFCT in OCT images.LocBscan-3D excels in precise fovea localization even with high myopia.LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group.Combining both models offers a robust SFCT assessment approach,promising efficiency and accuracy for large-scale studies and clinical use.
基金supported by the National Key Researchand Development Program of China(No.2016YFC1301002 to Jianzeng Dong)the National Natural Science Foundation of China(No.81901841 to Dongdong Deng,No.81671650 and No.81971569 to Yi He,No.61527811 to Ling Xia)+1 种基金the Key Research and Development Program of Zhejiang Province(No.2020C03016 to Ling Xia)Dongdong Deng also acknowledges support from Dalian University of Technology(No.DUT18RC(3)068)。
文摘Numerous methods have been published to segment the infarct tissue in theleft ventricle, most of them either need manual work, post-processing, or suffer from poorreproducibility. We proposed an automatic segmentation method for segmenting the infarct tissue irleft ventricle with myocardial infarction. Cardiac images of a total of 60 diseased hearts (55 humanhearts and 5 porcine hearts) were used in this study. The epicardial and endocardial boundariesof the ventricles in every 2D slice of the cardiac magnetic resonance with late gadoliniumenhancement images were manually segmented. The subsequent pipeline of infarct tissuesegmentation is fully automatic. The segmentation results with the automatic algorithm proposed inthis paper were compared to the consensus ground truth. The median of Dice overlap between ourautomatic method and the consensus ground truth is 0.79. We also compared the automatic methodwith the consensus ground truth using different image sources from diferent centers with diferentscan parameters and different scan machines. The results showed that the Dice overlap with thepublic dataset was 0.83, and the overall Dice overlap was 0.79. The results show that our method isrobust with respect to different MRI image sources, which were scanned by different centers withdifferent image collection parameters. The segmentation accuracy we obtained is comparable toor better than that of the conventional semi-automatic methods. Our segmentation method may beuseful for processing large amount of dataset in clinic.
基金This work was supported in part by National Natural Science Foundation of China(61771119 and 61901100)Hebei Provincial Natural Science Foundation of China(H2018501087 and H2019501010)Fundamental Research Funds for the Central Universities(N182304008).
文摘Segmentation of layers in retinal images obtained by optical coherence tomography(OCT)has become an important clinical tool to diagnose ophthalmic diseases.However,due to the sus-ceptibility to speckle noise and shadow of blood vessels etc.,the layer segmentation technology based on a single image still fail to reach a satisfactory level.We propose a combination method of structure interpolation and lateral mean filtering(SI-LMF)to improve the signal-to-noise ratio based on one retinal image.Before performing one-dimensional lateral mean filtering to remove noise,structure interpolation was operated to eliminate thickness fluctuations.Then,we used boundary growth method to identify boundaries.Compared with existing segmentations,the method proposed in this paper requires less data and avoids the influence of microsaccade.The automatic segmentation method was verified on the spectral domain OCT volume images obtained from four normal objects,which successfully identified the boundaries of 10 physio-logical layers,consistent with the results based on the manual determination.
基金National Natural Science Foundation of China(Nos.U2033201 and U1633105)。
文摘Skin defect inspection is one of the most significant tasks in the conventional process of aircraft inspection.This paper proposes a vision-based method of pixel-level defect detection,which is based on the Mask Scoring R-CNN.First,an attention mechanism and a feature fusion module are introduced,to improve feature representation.Second,a new classifier head—consisting of four convolutional layers and a fully connected layer—is proposed,to reduce the influence of information around the area of the defect.Third,to evaluate the proposed method,a dataset of aircraft skin defects was constructed,containing 276 images with a resolution of 960×720 pixels.Experimental results show that the proposed classifier head improves the detection and segmentation accuracy,for aircraft skin defect inspection,more effectively than the attention mechanism and feature fusion module.Compared with the Mask R-CNN and Mask Scoring R-CNN,the proposed method increased the segmentation precision by approximately 21%and 19.59%,respectively.These results demonstrate that the proposed method performs favorably against the other two methods of pixellevel aircraft skin defect detection.
文摘Microwave technology offers the possibility for pre-hospital stroke detection as we have previously demonstrated using non-imaging diagnostics. The focus in this paper is on image-based diagnostics wherein the technical and computational complexities of image reconstruction are a challenge for clinical realization. Herein we investigate whether information about a patient’s brain anatomy obtained prior to a stroke event can be used to facilitate image-based stroke diagnostics. A priori information can be obtained by segmenting the patient’s head tissues from magnetic resonance images. Expert manual segmentation is presently the gold standard, but it is laborious and subjective. A fully automatic method is thus desirable. This paper presents an evaluation of several such methods using both synthetic magnetic resonance imaging (MRI) data and real data from four healthy subjects. The segmentation was performed on the full 3D MRI data, whereas the electromagnetic evaluation was performed using a 2D slice. The methods were evaluated in terms of: i) tissue classification accuracy over all tissues with respect to ground truth, ii) the accuracy of the simulated electromagnetic wave propagation through the head, and iii) the accuracy of the image reconstruction of the hemorrhage. The segmentation accuracy was measured in terms of the degree of overlap (Dice score) with the ground truth. The electromagnetic simulation accuracy was measured in terms of signal deviation relative to the simulation based on the ground truth. Finally, the image reconstruction accuracy was measured in terms of the Dice score, relative error of dielectric properties, and visual comparison between the true and reconstructed intracerebral hemorrhage. The results show that accurate segmentation of tissues (Dice score = 0.97) from the MRI data can lead to accurate image reconstruction (relative error = 0.24) for the intracerebral hemorrhage in the subject’s brain. They also suggest that accurate automated segmentation can be used as a surrogate for manual segmentation and can facilitate the rapid diagnosis of intracerebral hemorrhage in stroke patients using a microwave imaging system.
基金This work was funded by the National Natural Science Foundation of Chinaunder Grant 62073305the Fundamental Research Funds for the Central Universities,China University of Geosciences(Wuhan)(Nos.CUG170610 and CUGGC02).
文摘In this paper,we propose a fully Soft Bionic Grasping Device(SBGD),which has advantages in automatically adjusting the grasping range,variable stiffness,and controllable bending shape.This device consists of soft gripper structures and a soft bionic bracket structure.We adopt the local thin-walled design in the soft gripper structures.This design improves the grippers’bending efficiency,and imitate human finger’s segmental bending function.In addition,this work also proposes a pneumatic soft bionic bracket structure,which not only can fix grippers,but also can automatically adjust the grasping space by imitating the human adjacent fingers’opening and closing movements.Due to the above advantages,the SBGD can grasp larger or smaller objects than the regular grasping devices.Particularly,to grasp small objects reliably,we further present a new Pinching Grasping(PG)method.The great performance of the fully SBGD is verified by experiments.This work will promote innovative development of the soft bionic grasping robots,and greatly meet the applications of dexterous grasping multi-size and multi-shape objects.
基金supported by Science and Technology Research Youth Project of Chongqing Municipal Education Commission(No.KJQN202301104)Cooperative Project between universities in Chongqing and Affiliated Institutes of Chinese Academy of Sciences(No.HZ2021011)+1 种基金Chongqing Municipal Science and Technology Commission Technology Innovation and Application Development Special Project(No.2022TIAD-KPX0040)Action Plan for Quality Development of Chongqing University of Technology Graduate Education(Grant No.gzlcx20242014).
文摘Instance segmentation is crucial in various domains,such as autonomous driving and robotics.However,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge devices.Therefore,it is essential to enhance detection speed while maintaining high accuracy.In this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving applications.Based on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature information.We experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)networks.Our experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.
文摘An accurate segmentation and quantification of the superficial foveal avascular zone(sFAZ)is important to facilitate the diagnosis and treatment of many retinal diseases,such as diabetic retinopathy and retinal vein occlusion.We proposed a method based on deep learning for the automatic segmentation and quantification of the sFAZ in optical coherence tomography angiography(OCTA)images with robustness to brightness and contrast(B/C)variations.A dataset of 405 OCTA images from 45 participants was acquired with Zeiss Cirrus HD-OCT 5000 and the ground truth(GT)was manually segmented subsequently.A deep learning network with an encoder–decoder architecture was created to classify each pixel into an sFAZ or non-sFAZ class.Subsequently,we applied largestconnected-region extraction and hole-filling to fine-tune the automatic segmentation results.A maximum mean dice similarity coefficient(DSC)of 0.976±0.011 was obtained when the automatic segmentation results were compared against the GT.The correlation coefficient between the area calculated from the automatic segmentation results and that calculated from the GT was 0.997.In all nine parameter groups with various brightness/contrast,all the DSCs of the proposed method were higher than 0.96.The proposed method achieved better performance in the sFAZ segmentation and quantification compared to two previously reported methods.In conclusion,we proposed and successfully verified an automatic sFAZ segmentation and quantification method based on deep learning with robustness to B/C variations.For clinical applications,this is an important progress in creating an automated segmentation and quantification applicable to clinical analysis.
基金the National Natural Science Foundation of China(Nos.U21A20117,U1560207 and 52003039)the National Key R&D Program of China(No.2017YFB0304402)+2 种基金the Fundamental Research Funds for the Central Universities(Nos.N2125018 and N2109001)the Liaoning Natural Science Foundation(No.2022-MS-365)the Liaoning Innovative Research Team in University(No.LT2017011).
文摘The internal quality inspection of the continuous casting billets is very important,and mis-inspection will seriously affect the subsequent production process.The UNet-VGG16 transfer learning model was used for semantic segmentation of the central shrinkage defects of the continuous casting billets.The automatic recognition accuracy of the central shrinkage defects of the continuous casting billets reaches more than 0.9.We use the minimum circumscribed rectangle to quantify the geometric dimensions such as length,width and area of the central shrinkage defects and use the threshold method to rate the central shrinkage defects of the continuous casting billets.The results show that all the testing images are rated correctly,and this method achieves the automatic recognition and intelligent analysis of the central shrinkage defects of the continuous casting billets.
基金This research was funded by the National Key Research and Development Program of China(No.2017YFC1501204)the National Natural Science Foundation of China(No.51678536)+4 种基金the Guangdong Innovative and Entrepreneurial Research Team Program(2016ZT06N340)the Program for Science and Technology Innovation Talents in Universities of Henan Province(Grant No.19HASTIT043)the Outstanding Young Talent Research Fund of Zhengzhou University(1621323001)the Program for Innovative Research Team(in Science and Technology)in University of Henan Province(18IRTSTHN007)the Research on NonDestructive Testing(NDT)and Rapid Evaluation Technology for Grouting Quality of Prestressed Ducts(Contract No.HG-GCKY-01-002).The authors would like to thank for these financial supports.
文摘Nowadays, asphalt road has dominated highways around the world. Among various defects of asphalt road, crackshave been paid more attention, since cracks often cause major engineering and personnel safety incidents. Currentmanual crack inspection methods are time-consuming and labor-intensive, and most segmentation methods cannot detect cracks at the pixel level. This paper proposes an intelligent segmentation and measurement model basedon the modified Mask R-CNN algorithm to automatically and accurately detect asphalt road cracks. The modelproposed in this paper mainly includes a convolutional neural network (CNN), an optimized region proposalnetwork (RPN), a region of interest (RoI) Align layer, a candidate area classification network and a Mask branch offully convolutional network (FCN). The ratio and size of anchors in the RPN are adjusted to improve the accuracyand efficiency of segmentation. Soft non-maximum suppression (Soft-NMS) algorithm is developed to improvethe segmentation accuracy. A dataset including 8,689 images (512× 512 pixels) of asphalt cracks is established andthe road crack is manually marked. Transfer learning is used to initialize the model parameters in the trainingprocess. To optimize the model training parameters, multiple comparison experiments are performed, and the testresults show that the mean average precision (mAP) value and F1-score of the optimal trained model are 0.952 and0.949. Subsequently, the robustness verification test and comparative test of the trained model are conducted andthe topological features of the crack are extracted. Then, the damage area, length and average width of the crackare measured automatically and accurately at pixel level. More importantly, this paper develops an automatic crackdetection platform for asphalt roads to automatically extract the number, area, length and average width of cracks,which can significantly improve the crack detection efficiency for the road maintenance industry.
