CNN based Anthropomorphic Model Observer for Defect Localization

Model Observers (MO) are algorithms designed to evaluate and optimize the parameters of newly developed medical imaging technologies by providing a measure of human accuracy for a given diagnostic task. If designed well, these algorithms can expedite and reduce the expenses of coordinating sessions with radiologists to evaluate the diagnosis potential of such reconstruction technologies. During the last decade, classic machine learning techniques along with feature engineering have proved to be a good MO choice by allowing the models to be trained to detect or localize defects and therefore potentially reduce the extent of needed human observer studies. More recently, and with the developments in computer processing speed and capabilities, Convolutional Neural Networks (CNN) have been introduced as MOs eliminating the need of feature engineering. In this paper, we design, train and evaluate the accuracy of a fully convolutional U-Net structure as a MO for a defect forced-localization task in simulated images. This work focuses on the optimization of parameters, hyperparameters and choice of objective functions for CNN model training. Results are shown in the form of human accuracy vs model accuracy as well as efficiencies with respect to the ideal observer, and reveal a strong agreement between the human and the MO for the chosen defect localization task.