Deep Learning Methods for Abnormality Detection and Segmentation in Computed Tomography and Magnetic Resonance Images

It appears your Web browser is not configured to display PDF files. Download adobe Acrobat or click here to download the PDF file.

Click here to download the PDF file.


Zabihollahy, Fatemeh




In this dissertation, I describe development of novel deep learning (DL)-based methodologies for the detection and segmentation of clinical abnormalities including renal lesions, left ventricle (LV) scar, and prostate cancer (PCa) in 3D computed tomography (CT) scans and magnetic resonance (MR) images. In the first phase, I developed a decision fusion of patch-based convolutional neural network (CNN) for renal masses classification into cyst versus solid. The solid renal masses were then categorized into benign and malignant using an image-based CNN. These approaches were selected to capture local and global features of the renal masses including intensity, texture, shape, and size, which are key features used by radiologists to classify renal masses. Results demonstrated that automated assessment of renal mass with moderate-to-high degrees of accuracy is feasible. In the second phase, I designed a novel algorithm that comprehensively learns and integrates inter- and intra-slice features from 3D late gadolinium enhancement (LGE)-MR images and allows to accurately and efficiently delineate LV scar fully automatically. In the proposed method, three U-Nets were trained using LGE-MR images extracted from transversal, coronal, and sagittal directions to learn the description of LV scar from different views and the predicted results were combined through majority voting system for the final segmentation. This algorithm benefited from isotropic property of the voxels in 3D MRI that permits for multiplanar reformation. In the third phase, I described a U-Net-based methodology to segment prostate zones from T2-weighted (T2W) and apparent diffusion coefficient (ADC) map prostate MR images as a fundamental requirement for automated diagnosis of PCa. This work is the first attempt for prostate zonal segmentation using ADC map MR images. Furthermore, I presented an ensemble learning system for fully automated localization of peripheral zone PCa from the ADC map. The ensemble learning model allowed accurate PCa detection, which was not possible using one network due to the complexity of the decision boundary. Our results confirmed that automated PCa detection and segmentation using ADC map MR image are feasible, highly sensitive and can be performed rapidly.


Education - Sciences
Education - Tests and Measurements




Carleton University


Fatemeh Zabihollahy

Thesis Degree Name: 

Doctor of Philosophy: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Engineering, Electrical and Computer

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).