深度学习自动分割中的置信度估计,用于放射治疗的磁共振成像中的脑部风险器官

IF 2 4区 医学 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Nouf M. Alzahrani, Ann M. Henry, Bashar M. Al-Qaisieh, Louise J. Murray, Michael G. Nix
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引用次数: 0

摘要

目的我们建立了一种名为 "自动置信度"(AutoConfidence,ACo)的新型人工智能驱动质量保证方法,在没有金标准分割的情况下按象素估算分割置信度,从而实现稳健、高效的自动分割(AS)审查。我们使用内部和外部(第三方)AS 模型,在 MRI 上的脑 OAR AS 中演示了这种方法。方法从本地临床队列中随机选取 32 例回顾性 MRI 计划胶质瘤病例进行 ACo 训练。对生成器进行对抗性训练,以生成内部自动分割(IAS),并根据输入的 MRI,使用判别器估算体素方面的 IAS 不确定性。为每个建议的分割生成置信度图,供操作员在 AS 编辑中使用,并与 "与黄金标准的差异 "误差图进行比较。九个病例用于测试 ACo 在 IAS 上的性能,并与两个外部深度学习分割模型预测[低质量 AS 外部模型(EM-LQ)和高质量 AS 外部模型(EM-HQ)]进行验证。评估采用了马修相关系数(MCC)、假阳性率(FPR)、假阴性率(FNR)和视觉评估。结果ACo在所有OAR(镜片除外)的内部和外部分割中都表现出了卓越的性能。MCC在IAS和低质量外部分割(EM-LQ)上高于高质量外部分割(EM-HQ)。在 IAS 和 EM-LQ 上,平均 MCC(不包括镜片)从 0.6 到 0.9 不等,而平均 FPR 和 FNR 分别≤0.13 和≤0.21。结论ACo可以可靠地预测内部和外部生成的AS的不确定性和误差,证明了其作为独立的、无参照物的质量保证工具的潜力,可以帮助操作人员在放射治疗临床中提供稳健、高效的自动分割。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy

Automated confidence estimation in deep learning auto-segmentation for brain organs at risk on MRI for radiotherapy

Purpose

We have built a novel AI-driven QA method called AutoConfidence (ACo), to estimate segmentation confidence on a per-voxel basis without gold standard segmentations, enabling robust, efficient review of automated segmentation (AS). We have demonstrated this method in brain OAR AS on MRI, using internal and external (third-party) AS models.

Methods

Thirty-two retrospectives, MRI planned, glioma cases were randomly selected from a local clinical cohort for ACo training. A generator was trained adversarialy to produce internal autosegmentations (IAS) with a discriminator to estimate voxel-wise IAS uncertainty, given the input MRI. Confidence maps for each proposed segmentation were produced for operator use in AS editing and were compared with “difference to gold-standard” error maps. Nine cases were used for testing ACo performance on IAS and validation with two external deep learning segmentation model predictions [external model with low-quality AS (EM-LQ) and external model with high-quality AS (EM-HQ)]. Matthew's correlation coefficient (MCC), false-positive rate (FPR), false-negative rate (FNR), and visual assessment were used for evaluation. Edge removal and geometric distance corrections were applied to achieve more useful and clinically relevant confidence maps and performance metrics.

Results

ACo showed generally excellent performance on both internal and external segmentations, across all OARs (except lenses). MCC was higher on IAS and low-quality external segmentations (EM-LQ) than high-quality ones (EM-HQ). On IAS and EM-LQ, average MCC (excluding lenses) varied from 0.6 to 0.9, while average FPR and FNR were ≤0.13 and ≤0.21, respectively. For EM-HQ, average MCC varied from 0.4 to 0.8, while average FPR and FNR were ≤0.37 and ≤0.22, respectively.

Conclusion

ACo was a reliable predictor of uncertainty and errors on AS generated both internally and externally, demonstrating its potential as an independent, reference-free QA tool, which could help operators deliver robust, efficient autosegmentation in the radiotherapy clinic.

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来源期刊
CiteScore
3.60
自引率
19.00%
发文量
331
审稿时长
3 months
期刊介绍: Journal of Applied Clinical Medical Physics is an international Open Access publication dedicated to clinical medical physics. JACMP welcomes original contributions dealing with all aspects of medical physics from scientists working in the clinical medical physics around the world. JACMP accepts only online submission. JACMP will publish: -Original Contributions: Peer-reviewed, investigations that represent new and significant contributions to the field. Recommended word count: up to 7500. -Review Articles: Reviews of major areas or sub-areas in the field of clinical medical physics. These articles may be of any length and are peer reviewed. -Technical Notes: These should be no longer than 3000 words, including key references. -Letters to the Editor: Comments on papers published in JACMP or on any other matters of interest to clinical medical physics. These should not be more than 1250 (including the literature) and their publication is only based on the decision of the editor, who occasionally asks experts on the merit of the contents. -Book Reviews: The editorial office solicits Book Reviews. -Announcements of Forthcoming Meetings: The Editor may provide notice of forthcoming meetings, course offerings, and other events relevant to clinical medical physics. -Parallel Opposed Editorial: We welcome topics relevant to clinical practice and medical physics profession. The contents can be controversial debate or opposed aspects of an issue. One author argues for the position and the other against. Each side of the debate contains an opening statement up to 800 words, followed by a rebuttal up to 500 words. Readers interested in participating in this series should contact the moderator with a proposed title and a short description of the topic
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