Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours. Phys Med Biol 2024 Oct 25;69(21)
Date
10/17/2024Pubmed ID
39413822Pubmed Central ID
PMC11551967DOI
10.1088/1361-6560/ad87a6Scopus ID
2-s2.0-85207663553 (requires institutional sign-in at Scopus site) 1 CitationAbstract
Objective.Deep-learning auto-segmentation (DLAS) aims to streamline contouring in clinical settings. Nevertheless, achieving clinical acceptance of DLAS remains a hurdle in abdominal MRI, hindering the implementation of efficient clinical workflows for MR-guided online adaptive radiotherapy (MRgOART). Integrating automated contour quality assurance (ACQA) with automatic contour correction (ACC) techniques could optimize the performance of ACC by concentrating on inaccurate contours. Furthermore, ACQA can facilitate the contour selection process from various DLAS tools and/or deformable contour propagation from a prior treatment session. Here, we present the performance of novel DL-based 3D ACQA models for evaluating DLAS contours acquired during MRgOART.Approach.The ACQA model, based on a 3D convolutional neural network (CNN), was trained using pancreas and duodenum contours obtained from a research DLAS tool on abdominal MRIs acquired from a 1.5 T MR-Linac. The training dataset contained abdominal MR images, DL contours, and their corresponding quality ratings, from 103 datasets. The quality of DLAS contours was determined using an in-house contour classification tool, which categorizes contours as acceptable or edit-required based on the expected editing effort. The performance of the 3D ACQA model was evaluated using an independent dataset of 34 abdominal MRIs, utilizing confusion matrices for true and predicted classes.Main results.The ACQA predicted 'acceptable' and 'edit-required' contours at 72.2% (91/126) and 83.6% (726/868) accuracy for pancreas, and at 71.2% (79/111) and 89.6% (772/862) for duodenum contours, respectively. The model successfully identified false positive (extra) and false negative (missing) DLAS contours at 93.75% (15/16) and %99.7 (438/439) accuracy for pancreas, and at 95% (57/60) and 98.9% (91/99) for duodenum, respectively.Significance.We developed 3D-ACQA models capable of quickly evaluating the quality of DLAS pancreas and duodenum contours on abdominal MRI. These models can be integrated into clinical workflow, facilitating efficient and consistent contour evaluation process in MRgOART for abdominal malignancies.
Author List
Zarenia M, Zhang Y, Sarosiek C, Conlin R, Amjad A, Paulson EAuthors
Asma Amjad PhD Instructor in the Radiation Oncology department at Medical College of WisconsinEric Paulson PhD Chief, Professor in the Radiation Oncology department at Medical College of Wisconsin
Christina Sarosiek Medical Physicist Assistant II in the Radiation Oncology department at Medical College of Wisconsin
MESH terms used to index this publication - Major topics in bold
AbdomenAutomation
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Pancreas
Quality Assurance, Health Care
Quality Control
Radiotherapy, Image-Guided
