CNN based anthropomorphic model observer for defect localization

Iris Lorente; Craig K. Abbey; Jovan G. Brankov

doi:10.1117/12.2581119

15 February 2021 CNN based anthropomorphic model observer for defect localization

Iris Lorente, Craig K. Abbey, Jovan G. Brankov

Proceedings Volume 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment; 115990B (2021) https://doi.org/10.1117/12.2581119
Event: SPIE Medical Imaging, 2021, Online Only

Abstract

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.

Conference Presentation

Citation Download Citation

Iris Lorente, Craig K. Abbey, and Jovan G. Brankov "CNN based anthropomorphic model observer for defect localization", Proc. SPIE 11599, Medical Imaging 2021: Image Perception, Observer Performance, and Technology Assessment, 115990B (15 February 2021); https://doi.org/10.1117/12.2581119

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available