Deep learning-based automatic contour quality assurance for auto-segmented abdominal MR-Linac contours.

IF 3.3 3区 医学 Q2 ENGINEERING, BIOMEDICAL Physics in medicine and biology Pub Date : 2024-10-25 DOI:10.1088/1361-6560/ad87a6
Mohammad Zarenia, Ying Zhang, Christina Sarosiek, Renae Conlin, Asma Amjad, Eric Paulson
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Abstract

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.

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基于深度学习的自动轮廓质量保证,用于自动分割腹部 MR-Linac 轮廓。
目的深度学习自动分割(DLAS)旨在简化临床环境中的轮廓划分。然而,在腹部磁共振成像中,DLAS 的临床接受度仍是一个障碍,阻碍了磁共振引导下在线自适应放射治疗(MRgOART)高效临床工作流程的实施。将自动轮廓质量保证(ACQA)与自动轮廓校正(ACC)技术相结合,可以集中处理不准确的轮廓,从而优化 ACC 的性能。此外,ACQA 还能促进从各种 DLAS 工具和/或之前治疗过程中的可变形轮廓传播中选择轮廓的过程。在此,我们介绍了基于 DL 的新型 3D ACQA 模型的性能,用于评估在 MRgOART 期间获取的 DLAS 轮廓。ACQA 模型基于三维卷积神经网络 (CNN),使用研究 DLAS 工具在 1.5T MR-Linac 采集的腹部 MRI 上获得的胰腺和十二指肠轮廓进行训练。训练数据集包含来自 103 个数据集的腹部 MR 图像、DL 轮廓及其相应的质量评级。DLAS 轮廓的质量是通过内部的轮廓分类工具确定的,该工具根据预期的编辑工作量将轮廓分为可接受的和需要编辑的。利用真实和预测类别的混淆矩阵,使用 34 个腹部 MRI 的独立数据集评估了 3D ACQA 模型的性能。ACQA 预测胰腺轮廓 "可接受 "和 "需要编辑 "的准确率分别为 72.2%(91/136)和 83.6%(726/868),预测十二指肠轮廓的准确率分别为 71.2%(79/111)和 89.6%(772/862)。该模型成功识别了假阳性(额外)和假阴性(缺失)DLAS 轮廓,胰腺的准确率分别为 93.75% (15/16) 和 %99.7 (438/439),十二指肠的准确率分别为 95% (57/60) 和 98.9% (91/99)。我们开发的 3D-ACQA 模型能够快速评估腹部 MRI 上 DLAS 胰腺和十二指肠轮廓的质量。这些模型可集成到临床工作流程中,促进腹部恶性肿瘤 MRgOART 中高效、一致的轮廓评估过程。
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来源期刊
Physics in medicine and biology
Physics in medicine and biology 医学-工程:生物医学
CiteScore
6.50
自引率
14.30%
发文量
409
审稿时长
2 months
期刊介绍: The development and application of theoretical, computational and experimental physics to medicine, physiology and biology. Topics covered are: therapy physics (including ionizing and non-ionizing radiation); biomedical imaging (e.g. x-ray, magnetic resonance, ultrasound, optical and nuclear imaging); image-guided interventions; image reconstruction and analysis (including kinetic modelling); artificial intelligence in biomedical physics and analysis; nanoparticles in imaging and therapy; radiobiology; radiation protection and patient dose monitoring; radiation dosimetry
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