Automatic Pancreas Segmentation in CT Images Using EfficientNetV2 and Multi-Branch Structure  

作　　者：Panru Liang Guojiang Xin Xiaolei Yi Hao Liang Changsong Ding 

机构地区：[1]School of Informatics,Hunan University of Chinese Medicine,Changsha,410208,China [2]Department of Hepatobiliary Pancreatic Surgery,Changsha Eighth Hospital,Changsha,410100,China [3]School of Traditional Chinese Medicine,Hunan University of Chinese Medicine,Changsha,410208,China

出　　处：《Computers, Materials & Continua》2025年第5期2481-2504,共24页计算机、材料和连续体(英文)

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);the Changsha Natural Science Foundation Project(Grant No.kq2202265);the key project of the Hunan Provincial of Education(Grant No.22A0255).

摘　　要：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.

关 键 词：Pancreas segmentation efficientNetV2 multi-branch structure RE-PARAMETERIZATION 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]