2023/12/12 by Jean-Emmanuel Bibault, Jean‐Emmanuel Bibault, P Giraud +1 · 1 citation
Engineering · Medicine · Physics and Astronomy · #Advanced Radiotherapy Techniques #Medical Imaging and Analysis #Radiomics and Machine Learning in Medical Imaging
paper · pdf · doi:10.1093/bjr/tqad018
crossref issued 2023/12/12 · crossref published 2023/12/12 · crossref published-online 2023/12/12 · openalex publication_date 2023/12/12 · crossref created 2024/01/10 · crossref published-print 2024/01/23 · crossref deposited 2024/01/24 · openalex created_date 2025/10/10 · crossref indexed 2026/07/31 · openalex updated_date 2026/07/31
The segmentation of organs and structures is a critical component of radiation therapy planning, with manual segmentation being a laborious and time-consuming task. Interobserver variability can also impact the outcomes of radiation therapy. Deep neural networks have recently gained attention for their ability to automate segmentation tasks, with convolutional neural networks (CNNs) being a popular approach. This article provides a descriptive review of the literature on deep learning (DL) techniques for segmentation in radiation therapy planning. This review focuses on five clinical sub-sites and finds that U-net is the most commonly used CNN architecture. The studies using DL for image segmentation were included in brain, head and neck, lung, abdominal, and pelvic cancers. The majority of DL segmentation articles in radiation therapy planning have concentrated on normal tissue structures. N-fold cross-validation was commonly employed, without external validation. This research area is expanding quickly, and standardization of metrics and independent validation are critical to benchmarking and comparing proposed methods.