Speaker: Leihao Wei
Affiliation: Ph.D. Candidate
Via Zoom Only: https://uclahs.zoom.us/j/97999407761
Computed tomography (CT) plays an integral role in diagnosing and screening various types of diseases. A growing number of machine learning (ML) models have been developed for prediction and classification that utilize derived quantitative image features, thanks in part to the availability of large CT datasets and advances in medical image analysis. Researchers have classified disease severity using quantitative image features such as hand-crafted radiomic and deep features. Despite reporting high classification performance, these models typically do not generalize well. Variations in the appearance of CT scans caused by differences in acquisition and reconstruction parameters adversely impact the reproducibility of quantitative image features and the performance of machine learning algorithms. As a result, few ML algorithms have been used in clinical settings.
Mitigating the effects of varying CT acquisition and reconstruction parameters is a challenging inverse problem. Recent advances in deep learning have demonstrated that image translation and denoising models can achieve high per-pixel similarity metrics when compared to a target image. The purpose of this dissertation is to develop and evaluate two conditional generative models that mitigate the effects of working with CT scans acquired and reconstructed with a variety of parameters. The overarching hypothesis is that improved image quality results in better consistency in downstream tasks. In essence, these models attempt to learn the underlying conditional distribution on the normalized images (high-quality) given the un-normalized (low-quality) images. First, I propose a novel CT image normalization method based on a 3D conditional generative adversarial network (GAN) that utilizes a spectral-normalization algorithm. My model provides an end-to-end solution for normalizing scans acquired using different doses, slice thicknesses, and reconstruction kernels. This study demonstrates that the GAN is capable of mitigating the variability in image quality, quantitative image features, and lung nodule detection using an automated Computer-Aided-Detection (CAD) system. Second, I explore the use of a conditional normalizing flow-based model to incorporate uncertainty information during image translation. The model is capable of learning the explicit conditional density and generating several plausible image outputs, providing a means to reduce the distortions introduced by existing methods. This dissertation compares these two generative approaches, identifying their strengths and limitations when normalizing heterogeneous CT images and mitigating the effect of different acquisition and reconstruction parameters on downstream clinical tasks.
Leihao Wei is a Ph.D. candidate in Electrical and Computer Engineering at the University of California, Los Angeles, under the supervision of Prof. Gregory Pottie. He is currently a graduate student researcher at Medical & Imaging Informatics Group at UCLA. His research focuses on artificial intelligence in Computed Tomography image analysis. He was a software engineer intern at Facebook in the 2020 summer.
For more information, please contact Prof. Gregory Pottie ()
Date(s) - Jun 09, 2021
4:00 pm - 6:00 pm
Via Zoom Only
No location, Los Angeles