Automating Computed Tomography Analysis for Early Diagnosis of Neurological Diseases
AffiliationElectrical and Electronic Engineering
Document TypePhD thesis
Access StatusThis item is embargoed and will be available on 2022-08-03.
© 2020 Nandakishor Desai
Neurological diseases are diseases of the nervous system that occur due to structural or biochemical abnormalities in the brain and nervous system. A diverse set of neurological diseases with varied symptoms makes it complicated to diagnose them with a standard protocol. Nevertheless, medical imaging can play a significant role in their early diagnosis by providing an accurate visualisation of internal body structures. However, analysis of the medical images mostly involves significant human intervention in complex disease cases. This process is not only time-intensive, but also laborious, and exhibits inter- and intra-observer variances. To this end, this study contributes to automating the early diagnosis of neurological diseases from computed tomography images. The first contribution of the thesis involves early diagnosis of cerebral aneurysms from computed tomography angiograms. A large-scale computed tomography angiograms dataset is constructed to investigate the automated diagnosis of unruptured cerebral aneurysms. A novel convolutional neural network architecture is proposed and trained on the dataset to identify aneurysm voxels from the images and subsequently, diagnose its presence in the given image scan. The proposed approach achieves a sensitivity of 92% in diagnosing aneurysms and a dice score of 65.2% in their localisation, thus demonstrating the efficacy of the proposed work. The second focus is on Parkinson’s disease, a neurological disease affecting the control of body movements. It can cause significant speech impairment early its course. Therefore, analysing the abnormalities in vocal fold movements during phonation can be a useful indicator for early signs. Computed tomography is an efficient imaging modality that captures dynamic vocal fold movements with a good spatial and temporal resolution. Therefore, it allows for a direct assessment of the movements of vocal folds and associated structures. A large-scale image dataset is constructed by capturing computed tomography scans of the neck during vocalisation period. First, a basic image processing-based approach is proposed that helps to explore and identify clinically useful feature points from arytenoid cartilages supporting the vocal fold movements. Further, convolutional neural network-based object detector is trained to fully localise the arytenoid cartilages. Inter arytenoid distance feature is then extracted to demonstrate its utility in differentiating Parkinson’s patients from healthy controls. In this final part of the contribution, novel machine learning interpretability techniques based on canonical correlation analysis, are proposed that assist in interpreting the representations learned by convolutional neural networks designed for the specific medical image analysis tasks. A set of novel two-dimensional multiset canonical correlation analysis algorithms are proposed that effectively capture the linear relationships between learned feature representations within and between neural networks. Results are presented by employing the proposed interpretability techniques to analyse the learned representations of neural networks trained to segment cerebral aneurysms from computed tomography angiograms. In summary, the thesis contributes to automating the analysis of computed tomography images for early detection of neurological diseases.
KeywordsNeurological Diseases; Computed Tomography; Convolutional Neural Network; Cerebral Aneurysms; Parkinson's Disease; Machine Learning Interpretability
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