Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance)

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING
Mena Shenouda, Eyjólfur Gudmundsson, Feng Li, Christopher M. Straus, Hedy L. Kindler, Arkadiusz Z. Dudek, Thomas Stinchcombe, Xiaofei Wang, Adam Starkey, Samuel G. Armato III
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Abstract

The purpose of this study was to evaluate the impact of probability map threshold on pleural mesothelioma (PM) tumor delineations generated using a convolutional neural network (CNN). One hundred eighty-six CT scans from 48 PM patients were segmented by a VGG16/U-Net CNN. A radiologist modified the contours generated at a 0.5 probability threshold. Percent difference of tumor volume and overlap using the Dice Similarity Coefficient (DSC) were compared between the reference standard provided by the radiologist and CNN outputs for thresholds ranging from 0.001 to 0.9. CNN-derived contours consistently yielded smaller tumor volumes than radiologist contours. Reducing the probability threshold from 0.5 to 0.01 decreased the absolute percent volume difference, on average, from 42.93% to 26.60%. Median and mean DSC ranged from 0.57 to 0.59, with a peak at a threshold of 0.2; no distinct threshold was found for percent volume difference. The CNN exhibited deficiencies with specific disease presentations, such as severe pleural effusion or disease in the pleural fissure. No single output threshold in the CNN probability maps was optimal for both tumor volume and DSC. This study emphasized the importance of considering both figures of merit when evaluating deep learning-based tumor segmentations across probability thresholds. This work underscores the need to simultaneously assess tumor volume and spatial overlap when evaluating CNN performance. While automated segmentations may yield comparable tumor volumes to that of the reference standard, the spatial region delineated by the CNN at a specific threshold is equally important.

Abstract Image

用于胸膜间皮瘤分割的卷积神经网络:概率图阈值分析(CALGB 30901,联盟)
本研究旨在评估概率图阈值对使用卷积神经网络(CNN)生成的胸膜间皮瘤(PM)肿瘤划分的影响。使用 VGG16/U-Net CNN 对 48 名胸膜间皮瘤患者的 186 张 CT 扫描图像进行了分割。放射科医生以 0.5 的概率阈值修改生成的轮廓。在 0.001 至 0.9 的阈值范围内,比较了放射科医生提供的参考标准与 CNN 输出结果之间的肿瘤体积百分比差异和使用 Dice 相似性系数 (DSC) 的重叠度。CNN 导出的轮廓得出的肿瘤体积始终小于放射科医生的轮廓。概率阈值从 0.5 降至 0.01 后,肿瘤体积绝对百分比差异平均从 42.93% 降至 26.60%。DSC的中位数和平均值从0.57到0.59不等,在阈值为0.2时达到峰值;在体积百分比差异方面没有发现明显的阈值。CNN 在特定疾病(如严重胸腔积液或胸膜裂孔中的疾病)时表现出缺陷。对于肿瘤体积和 DSC 而言,CNN 概率图中没有一个输出阈值是最佳的。这项研究强调,在评估基于深度学习的跨概率阈值肿瘤分割时,必须同时考虑这两项数据的优劣。这项工作强调了在评估 CNN 性能时同时评估肿瘤体积和空间重叠的必要性。虽然自动分割可能会产生与参考标准相当的肿瘤体积,但 CNN 在特定阈值下划定的空间区域同样重要。
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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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