An image analysis approach to MRI brain tumour grading

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Development and application of quantitative image analysis for preclinical MRI research

The aim of this thesis is to develop quantitative analysis methods to validate MRI and improve the detection of tumour infiltration. The major components include a description of the development the quantitative methods to better validate imaging biomarkers and detect of infiltration of tumour cells into normal tissue, which were then applied to a mouse model of glioblastoma invasion. To do this, a new histology model, called Stacked In-plane Histology (SIH), was developed to allow a quantitative analysis of MRI. Validating imaging biomarkers for glioblastoma infiltration Cancer can be defined as a disease in which a group of abnormal cells grow uncontrollably, often with fatal outcomes. According to (Cancer research UK, 2019), there are more than 363,000 new cancer cases in the UK every year, an increase from the 990 cases reported daily in 2014-2016, with only half of all patients recovering. Glioblastoma (GB) is the most frequent and malignant form of primary brain tumours with a very poor prognosis. Even with the development of modern diagnostic strategies and new therapies, the five-year survival rate is just 5%, with the median survival time only 14 months. Unfortunately, glioblastoma can affect patients at any age, including young children, but has a peak occurrence between the ages of 65 and 75 years. The standard treatment for GB consists of surgical resection, followed by radiotherapy and chemotherapy. However, the infiltration of GB cells into healthy adjacent brain tissue is a major obstacle for successful treatment, making complete removal of a tumour by surgery a difficult task, with the potential for tumour recurrence. Magnetic Resonance Imaging (MRI) is a non-invasive, multipurpose imaging tool used for the diagnosis and monitoring of cancerous tumours. It can provide morphological, physiological, and metabolic information about the tumour. Currently, MRI is the standard diagnostic tool for GB before the pathological examination of tissue from surgical resection or biopsy specimens. The standard MRI sequences used for diagnosis of GB include T2-Weighted (T2W), T1-Weighted (T1W), Fluid-Attenuated Inversion Recovery (FLAIR), and Contrast Enhance T1 gadolinium (CE-T1) scans. These conventional scans are used to localize the tumour, in addition to associated oedema and necrosis. Although these scans can identify the bulk of the tumour, it is known that they do not detect regions infiltrated by GB cells. The MRI signal depends upon many physical parameters including water content, local structure, tumbling rates, diffusion, and hypoxia (Dominietto, 2014). There has been considerable interest in identifying whether such biologically indirect image contrasts can be used as non-invasive imaging biomarkers, either for normal biological functions, pathogenic processes or pharmacological responses to therapeutic interventions (Atkinson et al., 2001). In fact, when new MRI methods are proposed as imaging biomarkers of particular diseases, it is crucial that they are validated against histopathology. In humans, such validation is limited to a biopsy, which is the gold standard of diagnosis for most types of cancer. Some types of biopsies can take an image-guided approach using MRI, Computed Tomography (CT) or Ultrasound (US). However, a biopsy may miss the most malignant region of the tumour and is difficult to repeat. Biomarker validation can be performed in preclinical disease models, where the animal can be terminated immediately after imaging for histological analysis. Here, in principle, co-registration of the biomarker images with the histopathology would allow for direct validation. However, in practice, most preclinical validation studies have been limited to using simple visual comparisons to assess the correlation between the imaging biomarker and underlying histopathology. First objective (Chapter 5): Histopathology is the gold standard for assessing non-invasive imaging biomarkers, with most validation approaches involving a qualitative visual inspection. To allow a more quantitative analysis, previous studies have attempted to co-register MRI with histology. However, these studies have focused on developing better algorithms to deal with the distortions common in histology sections. By contrast, we have taken an approach to improve the quality of the histological processing and analysis, for example, by taking into account the imaging slice orientation and thickness. Multiple histology sections were cut in the MR imaging plane to produce a Stacked In-plane Histology (SIH) map. This approach, which is applied to the next two objectives, creates a histopathology map which can be used as the gold standard to quantitatively validate imaging biomarkers. Second objective (Chapter 6): Glioblastoma is the most malignant form of primary brain tumour and recurrence following treatment is common. Non-invasive MR imaging is an important component of brain tumour diagnosis and treatment planning. Unfortunately, clinic MRI (T1W, T2W, CE-T1, and FLAIR) fails to detect regions of glioblastoma cell infiltration beyond the solid tumour region identified by contrast enhanced T1 scans. However, advanced MRI techniques such as Arterial Spin Labelling (ASL) could provide us with extra information (perfusion) which may allow better detection of infiltration. In order to assess whether local perfusion perturbation could provide a useful biomarker for glioblastoma cell infiltration, we quantitatively analysed the correlation between perfusion MRI (ASL) and stacked in-plane histology. This work used a mouse model of glioblastoma that mimics the infiltrative behaviour found in human patients. The results demonstrate the ability of perfusion imaging to probe regions of low tumour cell infiltration, while confirming the sensitivity limitations of clinic imaging modalities. Third objective (Chapter 7): It is widely hypothesised that Multiparametric MRI (mpMRI), can extract more information than is obtained from the constituent individual MR images, by reconstructing a single map that contains complementary information. Using the MRI and histology dataset from objective 2, we used a multi-regression algorithm to reconstruct a single map which was highly correlated (r>0.6) with histology. The results are promising, showing that mpMRI can better predict the whole tumour region, including the region of tumour cell infiltration

Automatic Segmentation of Intramedullary Multiple Sclerosis Lesions

Contexte: La moelle épinière est un composant essentiel du système nerveux central. Elle contient des neurones responsables d’importantes fonctionnalités et assure la transmission d’informations motrices et sensorielles entre le cerveau et le système nerveux périphérique. Un endommagement de la moelle épinière, causé par un choc ou une maladie neurodégénérative, peut mener à un sérieux handicap, pouvant entraîner des incapacités fonctionnelles, de la paralysie et/ou de la douleur. Chez les patients atteints de sclérose en plaques (SEP), la moelle épinière est fréquemment affectée par de l’atrophie et/ou des lésions. L’imagerie par résonance magnétique (IRM) conventionnelle est largement utilisée par des chercheurs et des cliniciens pour évaluer et caractériser, de façon non-invasive, des altérations micro-structurelles. Une évaluation quantitative des atteintes structurelles portées à la moelle épinière (e.g. sévérité de l’atrophie, extension des lésions) est essentielle pour le diagnostic, le pronostic et la supervision sur le long terme de maladies, telles que la SEP. De plus, le développement de biomarqueurs impartiaux est indispensable pour évaluer l’effet de nouveaux traitements thérapeutiques. La segmentation de la moelle épinière et des lésions intramédullaires de SEP sont, par conséquent, pertinentes d’un point de vue clinique, aussi bien qu’une étape nécessaire vers l’interprétation d’images RM multiparamétriques. Cependant, la segmentation manuelle est une tâche extrêmement chronophage, fastidieuse et sujette à des variations inter- et intra-expert. Il y a par conséquent un besoin d’automatiser les méthodes de segmentations, ce qui pourrait faciliter l’efficacité procédures d’analyses. La segmentation automatique de lésions est compliqué pour plusieurs raisons: (i) la variabilité des lésions en termes de forme, taille et position, (ii) les contours des lésions sont la plupart du temps difficilement discernables, (iii) l’intensité des lésions sur des images MR sont similaires à celles de structures visiblement saines. En plus de cela, réaliser une segmentation rigoureuse sur l’ensemble d’une base de données multi-centrique d’IRM est rendue difficile par l’importante variabilité des protocoles d’acquisition (e.g. résolution, orientation, champ de vue de l’image). Malgré de considérables récents développements dans le traitement d’images MR de moelle épinière, il n’y a toujours pas de méthode disponible pouvant fournir une segmentation rigoureuse et fiable de la moelle épinière pour un large spectre de pathologies et de protocoles d’acquisition. Concernant les lésions intramédullaires, une recherche approfondie dans la littérature n’a pas pu fournir une méthode disponible de segmentation automatique. Objectif: Développer un système complètement automatique pour segmenter la moelle épinière et les lésions intramédullaires sur des IRM conventionnelles humaines. Méthode: L’approche présentée est basée de deux réseaux de neurones à convolution mis en cascade. La méthode a été pensée pour faire face aux principaux obstacles que présentent les données IRM de moelle épinière. Le procédé de segmentation a été entrainé et validé sur une base de données privée composée de 1943 images, acquises dans 30 différents centres avec des protocoles hétérogènes. Les sujets scannés comportent 459 sujets sains, 471 patients SEP et 112 avec d’autres pathologies affectant la moelle épinière. Le module de segmentation de la moelle épinière a été comparé à une méthode existante reconnue par la communauté, PropSeg. Résultats: L’approche basée sur les réseaux de neurones à convolution a fourni de meilleurs résultats que PropSeg, atteignant un Dice médian (intervalle inter-quartiles) de 94.6 (4.6) vs. 87.9 (18.3) %. Pour les lésions, notre segmentation automatique a permis d'obtenir un Dice de 60.0 (21.4) % en le comparant à la segmentation manuelle, un ratio de vrai positifs de 83 (34) %, et une précision de 77 (44) %. Conclusion: Une méthode complètement automatique et innovante pour segmenter la moelle épinière et les lésions SEP intramédullaires sur des données IRM a été conçue durant ce projet de maîtrise. La méthode a été abondamment validée sur une base de données clinique. La robustesse de la méthode de segmentation de moelle épinière a été démontrée, même sur des cas pathologiques. Concernant la segmentation des lésions, les résultats sont encourageants, malgré un taux de faux positifs relativement élevé. Je crois en l’impact que peut potentiellement avoir ces outils pour la communauté de chercheurs. Dans cette optique, les méthodes ont été intégrées et documentées dans un logiciel en accès-ouvert, la “Spinal Cord Toolbox”. Certains des outils développés pendant ce projet de Maîtrise sont déjà utilisés par des analyses d’études cliniques, portant sur des patients SEP et sclérose latérale amyotrophique.----------ABSTRACT Context: The spinal cord is a key component of the central nervous system, which contains neurons responsible for complex functions, and ensures the conduction of motor and sensory information between the brain and the peripheral nervous system. Damage to the spinal cord, through trauma or neurodegenerative diseases, can lead to severe impairment, including functional disabilities, paralysis and/or pain. In multiple sclerosis (MS) patients, the spinal cord is frequently affected by atrophy and/or lesions. Conventional magnetic resonance imaging (MRI) is widely used by researchers and clinicians to non-invasively assess and characterize spinal cord microstructural changes. Quantitative assessment of the structural damage to the spinal cord (e.g. atrophy severity, lesion extent) is essential for the diagnosis, prognosis and longitudinal monitoring of diseases, such as MS. Furthermore, the development of objective biomarkers is essential to evaluate the effect of new therapeutic treatments. Spinal cord and intramedullary MS lesions segmentation is consequently clinically relevant, as well as a necessary step towards the interpretation of multi-parametric MR images. However, manual segmentation is highly time-consuming, tedious and prone to intra- and inter-rater variability. There is therefore a need for automated segmentation methods to facilitate the efficiency of analysis pipelines. Automatic lesion segmentation is challenging for various reasons: (i) lesion variability in terms of shape, size and location, (ii) lesion boundaries are most of the time not well defined, (iii) lesion intensities on MR data are confounding with those of normal-appearing structures. Moreover, achieving robust segmentation across multi-center MRI data is challenging because of the broad variability of data features (e.g. resolution, orientation, field of view). Despite recent substantial developments in spinal cord MRI processing, there is still no method available that can yield robust and reliable spinal cord segmentation across the very diverse spinal pathologies and data features. Regarding the intramedullary lesions, a thorough search of the relevant literature did not yield available method of automatic segmentation. Goal: To develop a fully-automatic framework for segmenting the spinal cord and intramedullary MS lesions from conventional human MRI data. Method: The presented approach is based on a cascade of two Convolutional Neural Networks (CNN). The method has been designed to face the main challenges of ‘real world’ spinal cord MRI data. It was trained and validated on a private dataset made up of 1943 MR volumes, acquired in different 30 sites with heterogeneous acquisition protocols. Scanned subjects involve 459 healthy controls, 471 MS patients and 112 with other spinal pathologies. The proposed spinal cord segmentation method was compared to a state-of-the-art spinal cord segmentation method, PropSeg. Results: The CNN-based approach achieved better results than PropSeg, yielding a median (interquartile range) Dice of 94.6 (4.6) vs. 87.9 (18.3) % when compared to the manual segmentation. For the lesion segmentation task, our method provided a median Dice-overlap with the manual segmentation of 60.0 (21.4) %, a lesion-based true positive rate of 83 (34) % and a lesion-based precision de 77 (44) %. Conclusion: An original fully-automatic method to segment the spinal cord and intramedullary MS lesions on MRI data has been devised during this Master’s project. The method was validated extensively against a clinical dataset. The robustness of the spinal cord segmentation has been demonstrated, even on challenging pathological cases. Regarding the lesion segmentation, the results are encouraging despite the fairly high false positive rate. I believe in the potential value of these developed tools for the research community. In this vein, the methods are integrated and documented into an open-source software, the Spinal Cord Toolbox. Some of the tools developed during this Master’s project are already integrated into automated analysis pipelines of clinical studies, including MS and Amyotrophic Lateral Sclerosis patients

Deteção automática de Espaços de Virchow-Robin em imagens de ressonância magnética

Dissertação de mestrado integrado em Engenharia Biomédica (área de especialização em Eletrónica Médica)Os Espaços de Virchow-Robin estão compreendidos entre as paredes das artérias perfurantes do encéfalo e meninges, estando preenchidos por líquido. Por vezes ficam dilatados, tornando-se visíveis em imagens de ressonância magnética, apesar das suas dimensões próximas da resolução dos scanners atuais. Ao longo dos anos tem sido colocada a hipótese de o número dessas estruturas dilatadas se correlacionarem com algumas doenças, por exemplo acidentes vasculares cerebrais ou demência. No entanto, estes estudos baseiam-se em contagens semi-quantitativas em algumas regiões de interesse, por visualização das imagens. Assim, pretendeu-se implementar um sistema automático que, utilizando imagens de ressonância magnética, fosse capaz de extrair regiões de interesse, nomeadamente os gânglios da base e a substância branca, e fazer a deteção e contagem de Espaços de Virchow-Robin. Nesse sentido construiu-se uma pipeline para o pré-processamento, englobando a correção da falta de homogeneidade, remoção do crânio, do ruído e normalização das intensidades. No pré-processamento e na extração dos gânglios da base utilizou-se software existente, mas para a segmentação da substância branca desenvolveu-se um algoritmo que utiliza Random Decision Forests. Já para a deteção dos Espaços de Virchow-Robin implementouse um algoritmo que modela as suas propriedades com Marked Point Process, e procura a configuração que melhor se adequa através da otimização com Reversible Jump Markov Chain Monte Carlo e simulated annealing. O algoritmo de extração de substância branca também demonstrou resultados positivos na segmentação de substância cinzenta, porém o método de remoção do crânio não excluiu alguns tecidos, fazendo com que os resultados da segmentação do líquido cefalorraquidiano fossem piores. Foi validado na base de dados MRBrainS, demonstrando robustez relativamente à presença de lesões da substância branca, desde que existisse a sequência FLAIR. Por seu lado, o algoritmo de deteção de Espaços de Virchow-Robin foi aplicado em imagens MPRAGE com resolução isotrópica de 1 mm. Apesar de não ter sido possível validar, observou-se que a sua performance foi superior na substância branca do que nos gânglios da base, devendo, no futuro, desenvolver-se filtros mais adequados para a segunda região de interesse. Também se desenvolveu uma aplicação para visualização das estruturas detetadas, e da sua distribuição espacial.Virchow-Robin Spaces surround the walls of the perforating arteries of the brain, being bounded by meninges and filled with cerebrospinal fluid. Sometimes they get dilated, becoming visible in magnetic resonance images, although their dimensions are near the current scanners’ resolution. Over the years, the hypothesis that the number of these dilated structures may be correlated with some diseases has been studied, for example with strokes or dementia. However, these studies are based on semi-quantitative counts in some regions of interest, by visualization of the images. Therefore, it was intended to implement an automatic system that, using magnetic resonance images, was able to extract regions of interest, namely the basal ganglia and the white matter, and detect and count dilated Virchow-Robin Spaces. In order to do so, it was built a pipeline for the pre-processing of the images, which included the inhomogeneity correction, skull stripping, denoising and intensities normalization. For the pre-processing procedures and extraction of the basal ganglia it was used already existent software, but for the segmentation of the white matter it was developed an algorithm that employs Random Decision Forests. For the detection of the Virchow-Robin Spaces it was implemented an algorithm that models their properties with a Marked Point Process, and searches for the best configuration of these structures among the candidates by optimization of the model with Reversible Jump Markov Chain Monte Carlo and simulated annealing. The white matter segmentation algorithm also demonstrated positive results in the segmentation of grey matter, but the skull stripping method wasn’t able to exclude some tissues, resulting in worse performance for the cerebrospinal fluid. It was validated in the MRBrainS database, demonstrating that it is robust in the presence of white matter lesions, if the FLAIR sequence is available. On the other hand, the algorithm for detecting dilated Virchow-Robin Spaces was applied in MPRAGE sequences acquired with isotropic resolution of 1 mm. Although it was not possible to validate, it was observed that the algorithm’s performance was superior in the white matter than in the basal ganglia, so, in the future, better filters for the second region of interest should be developed. It was, also, built an application to visualize the detected structures and their spatial distribution