mlsanityData to 7 Oct 2026

Automated GI tract segmentation using deep learning

arXiv 2206.11048 ↗First posted 22 Jun 2022eess.IV cs.CV cs.LGcited by 0

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Abstract (arXiv)

The job of Radiation oncologists is to deliver x-ray beams pointed toward the tumor and at the same time avoid the stomach and intestines. With MR-Linacs (magnetic resonance imaging and linear accelerator systems), oncologists can visualize the position of the tumor and allow for precise dose according to tumor cell presence which can vary from day to day. The current job of outlining the position of the stomach and intestines to adjust the X-ray beams direction for the dose delivery to the tumor while avoiding the organs. This is a time-consuming and labor-intensive process that can easily prolong treatments from 15 minutes to an hour a day unless deep learning methods can automate the segmentation process. This paper discusses an automated segmentation process using deep learning to make this process faster and allow more patients to get effective treatment.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 15 of its 29 references that are in the map. First posted on arXiv 22 Jun 2022.

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15 of 29 references.

References · 15

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3 more
  1. 2019IoU Loss for 2D/3D Object Detectioncited by 128
  2. 2020Normalization Techniques in Training DNNs: Methodology, Analysis and Applicationcited by 90
  3. 2022ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentationcited by 28

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