Open Access

ARTICLE

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

Panru Liang¹, Guojiang Xin^1,*, Xiaolei Yi², Hao Liang³, Changsong Ding¹

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

* Corresponding Author: Guojiang Xin. Email:

Computers, Materials & Continua 2025, 83(2), 2481-2504. https://doi.org/10.32604/cmc.2025.060961

Received 13 November 2024; Accepted 12 February 2025; Issue published 16 April 2025

Abstract

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.

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Keywords

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