Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,signifi...Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.展开更多
As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be ...As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.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
Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In...Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In recent years, coarse-to-fine methods have been widely used to alleviate class imbalance issue and improve pancreas segmentation accuracy. However,cascaded methods could be computationally intensive and the refined results are significantly dependent on the performance of its coarse segmentation results. To balance the segmentation accuracy and computational efficiency, we propose a Discriminative Feature Attention Network for pancreas segmentation, to effectively highlight pancreas features and improve segmentation accuracy without explicit pancreas location. The final segmentation is obtained by applying a simple yet effective post-processing step. Two experiments on both public NIH pancreas CT dataset and abdominal BTCV multi-organ dataset are individually conducted to show the effectiveness of our method for 2 D pancreas segmentation. We obtained average Dice Similarity Coefficient(DSC) of 82.82±6.09%, average Jaccard Index(JI) of 71.13± 8.30% and average Symmetric Average Surface Distance(ASD) of 1.69 ± 0.83 mm on the NIH dataset. Compared to the existing deep learning-based pancreas segmentation methods, our experimental results achieve the best average DSC and JI value.展开更多
基金supported by the Science and Technology Innovation Programof Hunan Province(Grant No.2022RC1021)the Hunan Provincial Natural Science Foundation Project(Grant No.2023JJ60124)+1 种基金the Changsha Natural Science Foundation Project(Grant No.kq2202265)the key project of the Hunan Provincial of Education(Grant No.22A0255).
文摘Automatic pancreas segmentation plays a pivotal role in assisting physicians with diagnosing pancreatic diseases,facilitating treatment evaluations,and designing surgical plans.Due to the pancreas’s tiny size,significant variability in shape and location,and low contrast with surrounding tissues,achieving high segmentation accuracy remains challenging.To improve segmentation precision,we propose a novel network utilizing EfficientNetV2 and multi-branch structures for automatically segmenting the pancreas fromCT images.Firstly,an EfficientNetV2 encoder is employed to extract complex and multi-level features,enhancing the model’s ability to capture the pancreas’s intricate morphology.Then,a residual multi-branch dilated attention(RMDA)module is designed to suppress irrelevant background noise and highlight useful pancreatic features.And re-parameterization Visual Geometry Group(RepVGG)blocks with amulti-branch structure are introduced in the decoder to effectively integrate deep features and low-level details,improving segmentation accuracy.Furthermore,we apply re-parameterization to the model,reducing computations and parameters while accelerating inference and reducing memory usage.Our approach achieves average dice similarity coefficient(DSC)of 85.59%,intersection over union(IoU)of 75.03%,precision of 85.09%,and recall of 86.57%on the NIH pancreas dataset.Compared with other methods,our model has fewer parameters and faster inference speed,demonstrating its enormous potential in practical applications of pancreatic segmentation.
基金supported by the National Natural Science Foundation of China[61772242,61976106,61572239]the China Postdoctoral Science Foundation[2017M611737]+3 种基金the Six Talent Peaks Project in Jiangsu Province[DZXX-122]the Jiangsu Province EmergencyManagement Science and Technology Project[YJGL-TG-2020-8]the Key Research and Development Plan of Zhenjiang City[SH2020011]Postgraduate Innovation Fund of Jiangsu Province[KYCX18_2257].
文摘As the pancreas only occupies a small region in the whole abdominal computed tomography(CT)scans and has high variability in shape,location and size,deep neural networks in automatic pancreas segmentation task can be easily confused by the complex and variable background.To alleviate these issues,this paper proposes a novel pancreas segmentation optimization based on the coarse-to-fine structure,in which the coarse stage is responsible for increasing the proportion of the target region in the input image through the minimum bounding box,and the fine is for improving the accuracy of pancreas segmentation by enhancing the data diversity and by introducing a new segmentation model,and reducing the running time by adding a total weights constraint.This optimization is evaluated on the public pancreas segmentation dataset and achieves 87.87%average Dice-Sørensen coefficient(DSC)accuracy,which is 0.94%higher than 86.93%,result of the state-of-the-art pancreas segmentation methods.Moreover,this method has strong generalization that it can be easily applied to other coarse-to-fine or one step organ segmentation tasks.
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 Ph.D. Research Startup Project of Minnan Normal University(KJ2021020)the National Natural Science Foundation of China(12090020 and 12090025)Zhejiang Provincial Natural Science Foundation of China(LSD19H180005)。
文摘Accurate pancreas segmentation is critical for the diagnosis and management of diseases of the pancreas. It is challenging to precisely delineate pancreas due to the highly variations in volume, shape and location. In recent years, coarse-to-fine methods have been widely used to alleviate class imbalance issue and improve pancreas segmentation accuracy. However,cascaded methods could be computationally intensive and the refined results are significantly dependent on the performance of its coarse segmentation results. To balance the segmentation accuracy and computational efficiency, we propose a Discriminative Feature Attention Network for pancreas segmentation, to effectively highlight pancreas features and improve segmentation accuracy without explicit pancreas location. The final segmentation is obtained by applying a simple yet effective post-processing step. Two experiments on both public NIH pancreas CT dataset and abdominal BTCV multi-organ dataset are individually conducted to show the effectiveness of our method for 2 D pancreas segmentation. We obtained average Dice Similarity Coefficient(DSC) of 82.82±6.09%, average Jaccard Index(JI) of 71.13± 8.30% and average Symmetric Average Surface Distance(ASD) of 1.69 ± 0.83 mm on the NIH dataset. Compared to the existing deep learning-based pancreas segmentation methods, our experimental results achieve the best average DSC and JI value.
