TY  - EJOU
AU  - Liang, Panru 
AU  - Xin, Guojiang 
AU  - Yi, Xiaolei 
AU  - Liang, Hao 
AU  - Ding, Changsong 

TI  - Automatic Pancreas Segmentation in CT Images Using EfficientNetV2 and Multi-Branch Structure
T2  - Computers, Materials \& Continua

PY  - 2025
VL  - 83
IS  - 2
SN  - 1546-2226

AB  - 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 from CT 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 a multi-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.
KW  - Pancreas segmentation; efficientNetV2; multi-branch structure; re-parameterization

DO  - 10.32604/cmc.2025.060961