Improvement of Deep Learning Model for Gastrointestinal Tract Segmentation Surgery

Hong Zhou; Yan Lou; Jize Xiong; Yixu Wang; Yuxiang Liu

doi:10.54097/fcis.v6i1.19

Authors

Hong Zhou
Yan Lou
Jize Xiong
Yixu Wang
Yuxiang Liu

DOI:

https://doi.org/10.54097/fcis.v6i1.19

Keywords:

MRI Segmentation, Machine Learning, Unet2.5D, Neural Networks, Deep Learning

Abstract

In 2019, approximately 5 million individuals were diagnosed with gastrointestinal tract cancer globally, with about half eligible for radiation therapy. This treatment, crucial for many patients, faces challenges due to the manual segmentation process required in newer technologies like MR-Linacs. This project, supported by the UW-Madison Carbone Cancer Center, aims to automate the segmentation of stomach and intestines in MRI scans using deep learning. The Unet2.5D model, specifically Unet2.5D(Se-ResNet50), has shown promising results, achieving a Dice Coefficient of 0.848. Successful implementation of this model could significantly expedite treatments, enabling higher radiation doses to tumors while minimizing exposure to healthy tissues, ultimately improving patient care and long-term cancer control.

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