It is widely accepted that medical imaging reconstruction strategies should be optimized by maximizing the chance of a radiologist making a correct diagnostic. This implies organizing costly sessions with doctors to evaluate the images and provide feedback. Model Observers (MO) are algorithms designed to act as human surrogates in evaluating and providing feedback as a measure of diagnostic accuracy, and should be tuned to make the same diagnostic as the human’s, regardless of the correctness. In this work, we use a previously trained and optimized Convolutional Neural Network (CNN) based MO to construct classification images that show how the diagnostic information is accessed by the MO in a form of a perceptual filter. A single MO was trained for a forced-localization task in simulated data with three different power-law noise backgrounds representing different levels of background variability. The classification images are computed in the same way as they would for a human observer using 10,000 simulated images with a defect. The frequency profile of the MO classification images show that frequency weights appear band-pass in nature and highly correlated to the frequency weights from the human observer classification images.
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.
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.
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