文献类型：期刊文献

英文题名：Extension-contraction transformation network for pancreas segmentation in abdominal CT scans

作者：Zheng, Yuxiang[1];Luo, Jianxu[1]

机构：[1]East China Univ Sci & Technol, Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China

年份：2023

卷号：152

外文期刊名：COMPUTERS IN BIOLOGY AND MEDICINE

收录：;EI(收录号：20225013251815);WOS:【SCI-EXPANDED(收录号:WOS:000906146100012)】；

基金：Acknowledgments The authors gratefully acknowledge the financial supports by the Science and Technology Commission of Shanghai Municipality under Grant No. 19511121203.

语种：英文

外文关键词：CT scans; Pancreas segmentation; Coarse-to-fine; Extension-contraction transformation; Deep neural network

摘要：Accurate and automatic pancreas segmentation from abdominal computed tomography (CT) scans is crucial for the diagnosis and prognosis of pancreatic diseases. However, the pancreas accounts for a relatively small portion of the scan and presents high anatomical variability and low contrast, making traditional automated segmentation methods fail to generate satisfactory results. In this paper, we propose an extension- contraction transformation network (ECTN) and deploy it into a cascaded two-stage segmentation framework for accurate pancreas segmenting. This model can enhance the perception of 3D context by distinguishing and exploiting the extension and contraction transformation of the pancreas between slices. It consists of an encoder, a segmentation decoder, and an extension-contraction (EC) decoder. The EC decoder is responsible for predicting the inter-slice extension and contraction transformation of the pancreas by feeding the extension and contraction information generated by the segmentation decoder; meanwhile, its output is combined with the output of the segmentation decoder to reconstruct and refine the segmentation results. Quantitative evaluation is performed on NIH Pancreas Segmentation (Pancreas-CT) dataset using 4-fold cross-validation. We obtained average Precision of 86.59 +/- 6.14% , Recall of 85.11 +/- 5.96%, Dice similarity coefficient (DSC) of 85.58 +/- 3.98%. and Jaccard Index (JI) of 74.99 +/- 5.86%. The performance of our method outperforms several baseline and state-of-the-art methods.