Automatic detection of contouring errors using convolutional neural networks.

Medical physics Pub Date : 2019-11-01 Epub Date: 2019-09-26 DOI:10.1002/mp.13814
Dong Joo Rhee, Carlos E Cardenas, Hesham Elhalawani, Rachel McCarroll, Lifei Zhang, Jinzhong Yang, Adam S Garden, Christine B Peterson, Beth M Beadle, Laurence E Court
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引用次数: 63

Abstract

Purpose: To develop a head and neck normal structures autocontouring tool that could be used to automatically detect the errors in autocontours from a clinically validated autocontouring tool.

Methods: An autocontouring tool based on convolutional neural networks (CNN) was developed for 16 normal structures of the head and neck and tested to identify the contour errors from a clinically validated multiatlas-based autocontouring system (MACS). The computed tomography (CT) scans and clinical contours from 3495 patients were semiautomatically curated and used to train and validate the CNN-based autocontouring tool. The final accuracy of the tool was evaluated by calculating the Sørensen-Dice similarity coefficients (DSC) and Hausdorff distances between the automatically generated contours and physician-drawn contours on 174 internal and 24 external CT scans. Lastly, the CNN-based tool was evaluated on 60 patients' CT scans to investigate the possibility to detect contouring failures. The contouring failures on these patients were classified as either minor or major errors. The criteria to detect contouring errors were determined by analyzing the DSC between the CNN- and MACS-based contours under two independent scenarios: (a) contours with minor errors are clinically acceptable and (b) contours with minor errors are clinically unacceptable.

Results: The average DSC and Hausdorff distance of our CNN-based tool was 98.4%/1.23 cm for brain, 89.1%/0.42 cm for eyes, 86.8%/1.28 cm for mandible, 86.4%/0.88 cm for brainstem, 83.4%/0.71 cm for spinal cord, 82.7%/1.37 cm for parotids, 80.7%/1.08 cm for esophagus, 71.7%/0.39 cm for lenses, 68.6%/0.72 for optic nerves, 66.4%/0.46 cm for cochleas, and 40.7%/0.96 cm for optic chiasm. With the error detection tool, the proportions of the clinically unacceptable MACS contours that were correctly detected were 0.99/0.80 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively. The proportions of the clinically acceptable MACS contours that were correctly detected were 0.81/0.60 on average except for the optic chiasm, when contours with minor errors are clinically acceptable/unacceptable, respectively.

Conclusion: Our CNN-based autocontouring tool performed well on both the publically available and the internal datasets. Furthermore, our results show that CNN-based algorithms are able to identify ill-defined contours from a clinically validated and used multiatlas-based autocontouring tool. Therefore, our CNN-based tool can effectively perform automatic verification of MACS contours.

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使用卷积神经网络自动检测轮廓误差。
目的:开发一种头颈部正常结构自动巡视工具,该工具可用于从临床验证的自动巡视工具中自动检测自动巡视中的错误。方法:为16个正常的头颈部结构开发了一种基于卷积神经网络(CNN)的自动巡视工具,并对其进行了测试,以识别临床验证的基于多器官的自动巡视系统(MACS)的轮廓误差。对3495名患者的计算机断层扫描(CT)扫描和临床轮廓进行了半自动策划,并用于训练和验证基于CNN的自动巡视工具。通过计算174次内部和24次外部CT扫描中自动生成的轮廓和医生绘制的轮廓之间的Sørensen Dice相似系数(DSC)和Hausdorff距离,评估了该工具的最终精度。最后,基于CNN的工具在60名患者的CT扫描中进行了评估,以研究检测轮廓失败的可能性。这些患者的轮廓绘制失败分为轻微错误或重大错误。检测轮廓误差的标准是通过分析两种独立情况下基于CNN和MACS的轮廓之间的DSC来确定的:(a)具有小误差的轮廓在临床上是可接受的,(b)具有小错误的轮廓在医学上是不可接受的。结果:我们基于CNN的工具的平均DSC和Hausdorff距离为:大脑98.4%/1.23cm,眼睛89.1%/0.42cm,下颌骨86.8%/1.28cm,脑干86.4%/0.8cm,脊髓83.4%/0.71cm,腮腺82.7%/1.37cm,食道80.7%/1.08cm,晶状体71.7%/0.39cm,视神经68.6%/0.72,耳蜗66.4%/0.46cm,视交叉40.7%/0.96cm。使用误差检测工具,当具有较小误差的轮廓分别为临床可接受/不可接受时,除视交叉外,正确检测到的临床不可接受MACS轮廓的比例平均为0.99/0.80。正确检测到的临床可接受MACS轮廓的比例平均为0.81/0.60,但视交叉除外,此时具有较小误差的轮廓分别为临床可接受/不可接受。结论:我们基于CNN的自动巡视工具在公开可用的数据集和内部数据集上都表现良好。此外,我们的结果表明,基于CNN的算法能够从临床验证和使用的基于多大西洋的自动漫游工具中识别出定义不清的轮廓。因此,我们基于CNN的工具可以有效地执行MACS轮廓的自动验证。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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