DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in
  MRI Scans
Ye Zhang Yulu Gong Dongji Cui Xinrui Li Xinyu Shen
Gastrointestinal (GI) tract cancers pose a global health challenge, demanding
precise radiotherapy planning for optimal treatment outcomes. This paper
introduces a cutting-edge approach to automate the segmentation of GI tract
regions in magnetic resonance imaging (MRI) scans. Leveraging advanced deep
learning architectures, the proposed model integrates Inception-V4 for initial
classification, UNet++ with a VGG19 encoder for 2.5D data, and Edge UNet for
grayscale data segmentation. Meticulous data preprocessing, including
innovative 2.5D processing, is employed to enhance adaptability, robustness,
and accuracy.
  This work addresses the manual and time-consuming segmentation process in
current radiotherapy planning, presenting a unified model that captures
intricate anatomical details. The integration of diverse architectures, each
specializing in unique aspects of the segmentation task, signifies a novel and
comprehensive solution. This model emerges as an efficient and accurate tool
for clinicians, marking a significant advancement in the field of GI tract
image segmentation for radiotherapy planning.