mlsanityData to 14 Oct 2026

DeepGI: An Automated Approach for Gastrointestinal Tract Segmentation in MRI Scans

arXiv 2401.15354 ↗First posted 27 Jan 2024eess.IV cs.CVcited by 13

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Abstract (arXiv)

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.

Key facts. Cited by 13 papers in the map (data through 14 Oct 2026). Builds on 5 of its 25 references that are in the map. First posted on arXiv 27 Jan 2024.

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5 of 25 references.

References · 5

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  3. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  4. 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,487
  5. 2022SuperCon: Supervised Contrastive Learning for Imbalanced Skin Lesion Classificationcited by 4

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