Towards a data-driven treatment of epilepsy: computational methods to overcome low-data regimes in clinical settings

Epilepsy is the most common neurological disorder, affecting around 1 % of the population. One third of patients with epilepsy are drug-resistant. If the epileptogenic zone can be localized precisely, curative resective surgery may be performed. However, only 40 to 70 % of patients remain seizure-free after surgery. Presurgical evaluation, which in part aims to localize the epileptogenic zone (EZ), is a complex multimodal process that requires subjective clinical decisions, often relying on a multidisciplinary team’s experience. Thus, the clinical pathway could benefit from data-driven methods for clinical decision support. In the last decade, deep learning has seen great advancements due to the improvement of graphics processing units (GPUs), the development of new algorithms and the large amounts of generated data that become available for training. However, using deep learning in clinical settings is challenging as large datasets are rare due to privacy concerns and expensive annotation processes. Methods to overcome the lack of data are especially important in the context of presurgical evaluation of epilepsy, as only a small proportion of patients with epilepsy end up undergoing surgery, which limits the availability of data to learn from. This thesis introduces computational methods that pave the way towards integrating data-driven methods into the clinical pathway for the treatment of epilepsy, overcoming the challenge presented by the relatively small datasets available. We used transfer learning from general-domain human action recognition to characterize epileptic seizures from video–telemetry data. We developed a software framework to predict the location of the epileptogenic zone given seizure semiologies, based on retrospective information from the literature. We trained deep learning models using self-supervised and semi-supervised learning to perform quantitative analysis of resective surgery by segmenting resection cavities on brain magnetic resonance images (MRIs). Throughout our work, we shared datasets and software tools that will accelerate research in medical image computing, particularly in the field of epilepsy

Multiparametric Magnetic Resonance Imaging Artificial Intelligence Pipeline for Oropharyngeal Cancer Radiotherapy Treatment Guidance

Oropharyngeal cancer (OPC) is a widespread disease and one of the few domestic cancers that is rising in incidence. Radiographic images are crucial for assessment of OPC and aid in radiotherapy (RT) treatment. However, RT planning with conventional imaging approaches requires operator-dependent tumor segmentation, which is the primary source of treatment error. Further, OPC expresses differential tumor/node mid-RT response (rapid response) rates, resulting in significant differences between planned and delivered RT dose. Finally, clinical outcomes for OPC patients can also be variable, which warrants the investigation of prognostic models. Multiparametric MRI (mpMRI) techniques that incorporate simultaneous anatomical and functional information coupled to artificial intelligence (AI) approaches could improve clinical decision support for OPC by providing immediately actionable clinical rationale for adaptive RT planning. If tumors could be reproducibly segmented, rapid response could be classified, and prognosis could be reliably determined, overall patient outcomes would be optimized to improve the therapeutic index as a function of more risk-adapted RT volumes. Consequently, there is an unmet need for automated and reproducible imaging which can simultaneously segment tumors and provide predictive value for actionable RT adaptation. This dissertation primarily seeks to explore and optimize image processing, tumor segmentation, and patient outcomes in OPC through a combination of advanced imaging techniques and AI algorithms. In the first specific aim of this dissertation, we develop and evaluate mpMRI pre-processing techniques for use in downstream segmentation, response prediction, and outcome prediction pipelines. Various MRI intensity standardization and registration approaches were systematically compared and benchmarked. Moreover, synthetic image algorithms were developed to decrease MRI scan time in an effort to optimize our AI pipelines. We demonstrated that proper intensity standardization and image registration can improve mpMRI quality for use in AI algorithms, and developed a novel method to decrease mpMRI acquisition time. Subsequently, in the second specific aim of this dissertation, we investigated underlying questions regarding the implementation of RT-related auto-segmentation. Firstly, we quantified interobserver variability for an unprecedented large number of observers for various radiotherapy structures in several disease sites (with a particular emphasis on OPC) using a novel crowdsourcing platform. We then trained an AI algorithm on a series of extant matched mpMRI datasets to segment OPC primary tumors. Moreover, we validated and compared our best model\u27s performance to clinical expert observers. We demonstrated that AI-based mpMRI OPC tumor auto-segmentation offers decreased variability and comparable accuracy to clinical experts, and certain mpMRI input channel combinations could further improve performance. Finally, in the third specific aim of this dissertation, we predicted OPC primary tumor mid-therapy (rapid) treatment response and prognostic outcomes. Using co-registered pre-therapy and mid-therapy primary tumor manual segmentations of OPC patients, we generated and characterized treatment sensitive and treatment resistant pre-RT sub-volumes. These sub-volumes were used to train an AI algorithm to predict individual voxel-wise treatment resistance. Additionally, we developed an AI algorithm to predict OPC patient progression free survival using pre-therapy imaging from an international data science competition (ranking 1st place), and then translated these approaches to mpMRI data. We demonstrated AI models could be used to predict rapid response and prognostic outcomes using pre-therapy imaging, which could help guide treatment adaptation, though further work is needed. In summary, the completion of these aims facilitates the development of an image-guided fully automated OPC clinical decision support tool. The resultant deliverables from this project will positively impact patients by enabling optimized therapeutic interventions in OPC. Future work should consider investigating additional imaging timepoints, imaging modalities, uncertainty quantification, perceptual and ethical considerations, and prospective studies for eventual clinical implementation. A dynamic version of this dissertation is publicly available and assigned a digital object identifier through Figshare (doi: 10.6084/m9.figshare.22141871)

Automated brain lesion segmentation in magnetic resonance images

In this thesis, we investigate the potential of automation in brain lesion segmentation in magnetic resonance images. We first develop a novel supervised method, which segments regions in magnetic resonance images using gated recurrent units, provided training data with pixel-wise annotations on what to segment is available. We improve on this method using the latest technical advances in the field of machine learning and insights on possible weaknesses of our method, and adapt it specifically for the task of lesion segmentation in the brain. We show the feasibility of our approach on multiple public benchmarks, consistently reaching positions at the top of the list of competing methods. Adapting our problem successfully to the problem of landmark localization, we show the generalizability of the approach. Moving away from large training cohorts with manual segmentations to data where it is only known that a certain pathology is present, we propose a weakly-supervised segmentation approach. Given a set of images with known pathology of a certain kind and a healthy reference set, our formulation can segment the difference of the two data distributions. Lastly, we show how information from already existing lesion maps can be extracted in a meaningful way by connecting lesions across time in longitudinal studies. We hence present a full tool set for the automated processing of lesions in magnetic resonance images

AUTO-DELINEATION OF OROPHARYNGEAL CLINICAL TARGET VOLUMES USING DEEP LEARNING TECHNIQUES

Head and neck intensity modulate radiation therapy allows for the delivery of high-precision radiotherapy by conforming radiation dose to the defined treatment targets achieving more accurate target dose distribution and better sparing of normal tissues. However, producing very precise treatment plans may be ineffective if the target volumes are not defined accurately. Furthermore, there are several reports of significant inter-observer variability when delineating these target volumes for head and neck cancers making this variability one of the largest sources of uncertainty in head and neck radiation therapy. The purpose of this study was to develop algorithms to automate target delineation for oropharyngeal cancer patients. Automating this delineation process could aid in reducing inter-observer variability and provide a venue for head and neck target delineation standardization in radiation therapy. These algorithms would be especially valuable for head and neck cancers where the observed variability is highest amongst radiation oncologists. An assessment of our head and neck section’s inter-observer clinical target volume delineation variability was conducted to quantify the variability in our algorithm’s inputs. We then developed two novel deep learning algorithms to auto-delineate high-risk and low-risk clinical target volumes. The predicted delineations for high-risk and low-risk clinical target volumes performed well in comparison to their respective ground-truth delineations. The quantitative analysis showed that the predicted volumes provided, on average, improved delineations when compared to the assessed inter-observer variability. Lastly, we investigated dosimetric differences on target coverage and normal tissues based on the physician delineated and deep learning auto-delineated low-risk target volumes. The percent volume receiving 95% of the prescribed dose on the original physician PTVs was found acceptable, per RTOG 1016 guidelines, on over 70% of auto-delineated plans. In addition, we found no significant difference in normal tissue doses between the physician and auto-delineated target plans. This study resulted in strong evidence that auto-delineated clinical target volumes could aid in the standardization of target delineation in radiation therapy. The target volume auto-delineation algorithms showed an improvement in overlap and dosimetric agreement with respect to the reported variability in the literature. Future studies may validate the clinical use of these algorithms