Region-specific deep learning models for accurate segmentation of rectal structures on post-chemoradiation T2w MRI: a multi-institutional, multi-reader study
Abstract
For locally advanced rectal cancers, in vivo radiological evaluation of tumor extent and regression after neoadjuvant therapy involves implicit visual identification of rectal structures on magnetic resonance imaging (MRI). Additionally, newer image-based, computational approaches (e.g., radiomics) require more detailed and precise annotations of regions such as the outer rectal wall, lumen, and perirectal fat. Manual annotations of these regions, however, are highly laborious and time-consuming as well as subject to inter-reader variability due to tissue boundaries being obscured by treatment-related changes (e.g., fibrosis, edema).
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- May 11, 2023
- Source ID
- 10.3389/fmed.2023.1149056
Entities
People
- Andrei S. Purysko
- David Liska
- Eric Marderstein
- Hoa Le
- Jacob T. Antunes
- Jayakrishna Gollamudi
- Kaustav Bera
- Prathyush Chirra
- Rajmohan Paspulati
- Satish E Viswanath
- Sharon L. Stein
- Thomas Desilvio
- William Hall
Organizations
- National Cancer Institute
- National Institute of Biomedical Imaging and Bioengineering
- National Institute of Diabetes and Digestive and Kidney Diseases