Pathology Segmentation using Distributional Differences to Images of Healthy Origin

Fully supervised segmentation methods require a large training cohort of already segmented images, providing information at the pixel level of each image. We present a method to automatically segment and model pathologies in medical images, trained solely on data labelled on the image level as either healthy or containing a visual defect. We base our method on CycleGAN, an image-to-image translation technique, to translate images between the domains of healthy and pathological images. We extend the core idea with two key contributions. Implementing the generators as residual generators allows us to explicitly model the segmentation of the pathology. Realizing the translation from the healthy to the pathological domain using a variational autoencoder allows us to specify one representation of the pathology, as this transformation is otherwise not unique. Our model hence not only allows us to create pixelwise semantic segmentations, it is also able to create inpaintings for the segmentations to render the pathological image healthy. Furthermore, we can draw new unseen pathology samples from this model based on the distribution in the data. We show quantitatively, that our method is able to segment pathologies with a surprising accuracy being only slightly inferior to a state-of-the-art fully supervised method, although the latter has per-pixel rather than per-image training information. Moreover, we show qualitative results of both the segmentations and inpaintings. Our findings motivate further research into weakly-supervised segmentation using image level annotations, allowing for faster and cheaper acquisition of training data without a large sacrifice in segmentation accuracy

Regional Brain Stem Atrophy in Idiopathic Parkinson's Disease Detected by Anatomical MRI

Idiopathic Parkinson's disease (PD) is a neurodegenerative disorder characterized by the dysfunction of dopaminergic dependent cortico-basal ganglia loops and diagnosed on the basis of motor symptoms (tremors and/or rigidity and bradykinesia). Post-mortem studies tend to show that the destruction of dopaminergic neurons in the substantia nigra constitutes an intermediate step in a broader neurodegenerative process rather than a unique feature of Parkinson's disease, as a consistent pattern of progression would exist, originating from the medulla oblongata/pontine tegmentum. To date, neuroimaging techniques have been unable to characterize the pre-symptomatic stages of PD. However, if such a regular neurodegenerative pattern were to exist, consistent damages would be found in the brain stem, even at early stages of the disease. We recruited 23 PD patients at Hoenn and Yahr stages I to II of the disease and 18 healthy controls (HC) matched for age. T1-weighted anatomical scans were acquired (MPRAGE, 1 mm3 resolution) and analyzed using an optimized VBM protocol to detect white and grey matter volume reduction without spatial a priori. When the HC group was compared to the PD group, a single cluster exhibited statistical difference (p<0.05 corrected for false detection rate, 4287 mm3) in the brain stem, between the pons and the medulla oblongata. The present study provides in-vivo evidence that brain stem damage may be the first identifiable stage of PD neuropathology, and that the identification of this consistent damage along with other factors could help with earlier diagnosis in the future. This damage could also explain some non-motor symptoms in PD that often precede diagnosis, such as autonomic dysfunction and sleep disorders

Deep generative modelling of the imaged human brain

Human-machine symbiosis is a very promising opportunity for the field of neurology given that the interpretation of the imaged human brain is a trivial feat for neither entity. However, before machine learning systems can be used in real world clinical situations, many issues with automated analysis must first be solved. In this thesis I aim to address what I consider the three biggest hurdles to the adoption of automated machine learning interpretative systems. For each issue, I will first elucidate the reader on its importance given the overarching narratives of both neurology and machine learning, and then showcase my proposed solutions to these issues through the use of deep generative models of the imaged human brain. First, I start by addressing what is an uncontroversial and universal sign of intelligence: the ability to extrapolate knowledge to unseen cases. Human neuroradiologists have studied the anatomy of the healthy brain and can therefore, with some success, identify most pathologies present on an imaged brain, even without having ever been previously exposed to them. Current discriminative machine learning systems require vast amounts of labelled data in order to accurately identify diseases. In this first part I provide a generative framework that permits machine learning models to more efficiently leverage unlabelled data for better diagnoses with either none or small amounts of labels. Secondly, I address a major ethical concern in medicine: equitable evaluation of all patients, regardless of demographics or other identifying characteristics. This is, unfortunately, something that even human practitioners fail at, making the matter ever more pressing: unaddressed biases in data will become biases in the models. To address this concern I suggest a framework through which a generative model synthesises demographically counterfactual brain imaging to successfully reduce the proliferation of demographic biases in discriminative models. Finally, I tackle the challenge of spatial anatomical inference, a task at the centre of the field of lesion-deficit mapping, which given brain lesions and associated cognitive deficits attempts to discover the true functional anatomy of the brain. I provide a new Bayesian generative framework and implementation that allows for greatly improved results on this challenge, hopefully, paving part of the road towards a greater and more complete understanding of the human brain