Lorente Iris, Abbey Craig K, Brankov Jovan G
ECE Department, Illinois Institute of Technology, Chicago, IL, USA 60616.
Department of Psychological and Brain Sciences, University of California, Santa Barbara CA 93106.
Proc SPIE Int Soc Opt Eng. 2020 Feb;11316. doi: 10.1117/12.2549687. Epub 2020 Mar 16.
Model Observers (MO) are algorithms designed to evaluate and optimize the parameters of new medical imaging reconstruction methodologies by providing a measure of human accuracy for a diagnostic task. In contrast with a computer-aided diagnosis system, MOs are not designed to outperform human diagnosis but only to find a defect if a radiologist would be able to detect it. These algorithms can economize and expedite the finding of optimal reconstruction parameters by reducing the number of sessions with expert radiologists, which are costly and prolonged. Convolutional Neural Networks (CNN or ConvNet) have been successfully used in the computer vision field for image classification, segmentation and video analytics. In this paper, we propose and test several U-Net configurations as MO for a defect localization task on synthetic images with different levels of correlated noisy backgrounds. Preliminary results show that the CNN based MO has potential and its accuracy correlates well with that of the human.
模型观察者（MO）是一种算法，旨在通过提供诊断任务的人类准确性度量来评估和优化新的医学成像重建方法的参数。与计算机辅助诊断系统不同，MO的设计目的不是超越人类诊断，而只是在放射科医生能够检测到缺陷时发现缺陷。这些算法可以通过减少与专家放射科医生的会诊次数来节省并加快找到最佳重建参数的过程，而专家会诊成本高昂且耗时较长。卷积神经网络（CNN或ConvNet）已在计算机视觉领域成功用于图像分类、分割和视频分析。在本文中，我们提出并测试了几种U-Net配置作为MO，用于在具有不同相关噪声背景水平的合成图像上进行缺陷定位任务。初步结果表明，基于CNN的MO具有潜力，其准确性与人类的准确性相关性良好。
