DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans
This addresses the manual and time-consuming segmentation process for clinicians in GI tract cancer treatment, representing an incremental advancement in domain-specific medical imaging.
The paper tackles the problem of automating gastrointestinal tract segmentation in MRI scans for radiotherapy planning, resulting in a unified deep learning model that integrates multiple architectures to improve efficiency and accuracy.
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.