Understanding and Mitigating Search Errors in 3D Volumetric Images

In the field of oncology, three-dimensional volumetric medical images provide radiologists with a detailed visual representation of various anatomical structures that facilitate the early detection and characterization of malignant lesions but at the cost of an increased search space. Recent work (Lago et al., 2021) establishes that human observers rely heavily on peripheral visual processing away from the point of fixation when searching for signals in 3D volumetric images. The searcher’s over-reliance on peripheral vision interacts strongly with how much of the volume they explore and with how much they report they have explored. Specifically, observers under-explore—as determined by the percentage of the volume covered by the Useful Field of View (UFOV)—and overestimate the percentage of volume they explored through self-report measures. Consequently, they miss small signals during the search. This thesis aims to elucidate the psychological factors mediating human under-exploration of 3D volumetric image data. The second thrust of this thesis is to investigate three solutions to mitigate the detrimental impact of under-exploration in 3D images. The first method is a 2D synthetic view of the 3D data that observers can utilize as additional information when performing the 3D search. I establish through behavioral measurements and a computational model simulating foveated vision how the 2D-S guides eye movements to suspicious regions in the 3D volume. In turn, this guidance allows observers to find the small signal that would otherwise be missed without the 2D-S adjunct. The second method involves a different type of search aid, a convolutional neural network, which acts as a computer-aided detection system to assist human observers during the 3D search. Like the 2D-S, it guides eye movements to suspicious regions in a 3D volumetric image that observers would have otherwise not looked at. The last method is inspired by the power of group decision-making. It investigates how combining multiple independent judgments from a group of searchers can lead to more exploration of the search space and a higher chance of detecting the small signal. Together, the body of work herein provides empirical results from laboratory studies to further our understanding of how humans interact with 3D imaging modalities with the goal of improving healthcare services relating to early cancer screenings

Proceedings Virtual Imaging Trials in Medicine 2024

This submission comprises the proceedings of the 1st Virtual Imaging Trials in Medicine conference, organized by Duke University on April 22-24, 2024. The listed authors serve as the program directors for this conference. The VITM conference is a pioneering summit uniting experts from academia, industry and government in the fields of medical imaging and therapy to explore the transformative potential of in silico virtual trials and digital twins in revolutionizing healthcare. The proceedings are categorized by the respective days of the conference: Monday presentations, Tuesday presentations, Wednesday presentations, followed by the abstracts for the posters presented on Monday and Tuesday

Deep Learning in Medical Image Analysis

The accelerating power of deep learning in diagnosing diseases will empower physicians and speed up decision making in clinical environments. Applications of modern medical instruments and digitalization of medical care have generated enormous amounts of medical images in recent years. In this big data arena, new deep learning methods and computational models for efficient data processing, analysis, and modeling of the generated data are crucially important for clinical applications and understanding the underlying biological process. This book presents and highlights novel algorithms, architectures, techniques, and applications of deep learning for medical image analysis