@Article{JML-3-445,
author = {Chen , JiaweiChen , WenjieZhu , Zhipeng and Ye , Qi},
title = {MAFE-Net: A Multi-Level Attention Feature Extraction Network for Pancreas Segmentation},
journal = {Journal of Machine Learning},
year = {2024},
volume = {3},
number = {4},
pages = {445--463},
abstract = {<p style="text-align: justify;">Accurate and automatic segmentation of the pancreas from abdominal computed tomography (CT)
scans is crucial for diagnosing and treating pancreatic diseases. However, the pancreas is a tiny target abdominal organ with high anatomical variability and low tissue contrast in CT scans, making segmentation tasks
challenging. To address this challenge, we propose a multilevel attention feature extraction network to segment the pancreas in abdominal CT images. Specifically, a multi-field attention convolution module (MFAC)
and a connection feature fusion module (CFF) are added to the encoding and decoding structure to improve
the extraction of pancreatic features. To further enhance the segmentation network’s extraction of pancreatic
edge features, we propose a decoding feature recall module (DFC), which can be migrated to other encoding
and decoding structures and pruned to capture pancreatic edge information better. We compared the performance of our method with that of the most advanced method on the NIH pancreatic segmentation dataset and
the challenging pancreatic cancer CT image dataset collected by the Zhujiang Hospital of Southern Medical
University. The experimental results show that the DSC of our method on NIH dataset and pancreatic cancer
dataset is 84.69% and 78.18% respectively, which is superior to the existing methods.</p>},
issn = {2790-2048},
doi = {https://doi.org/10.4208/jml.231101},
url = {http://global-sci.org/intro/article_detail/jml/23502.html}
}