Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho

Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.

Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..

Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks

Segmentation of both white matter lesions and deep grey matter structures is an important task in the quantification of magnetic resonance imaging in multiple sclerosis. Typically these tasks are performed separately: in this paper we present a single segmentation solution based on convolutional neural networks (CNNs) for providing fast, reliable segmentations of multimodal magnetic resonance images into lesion classes and normal-appearing grey- and white-matter structures. We show substantial, statistically significant improvements in both Dice coefficient and in lesion-wise specificity and sensitivity, compared to previous approaches, and agreement with individual human raters in the range of human inter-rater variability. The method is trained on data gathered from a single centre: nonetheless, it performs well on data from centres, scanners and field-strengths not represented in the training dataset. A retrospective study found that the classifier successfully identified lesions missed by the human raters. Lesion labels were provided by human raters, while weak labels for other brain structures (including CSF, cortical grey matter, cortical white matter, cerebellum, amygdala, hippocampus, subcortical GM structures and choroid plexus) were provided by Freesurfer 5.3. The segmentations of these structures compared well, not only with Freesurfer 5.3, but also with FSL-First and Freesurfer 6.0

Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review

Multiple Sclerosis (MS) is a type of brain disease which causes visual, sensory, and motor problems for people with a detrimental effect on the functioning of the nervous system. In order to diagnose MS, multiple screening methods have been proposed so far; among them, magnetic resonance imaging (MRI) has received considerable attention among physicians. MRI modalities provide physicians with fundamental information about the structure and function of the brain, which is crucial for the rapid diagnosis of MS lesions. Diagnosing MS using MRI is time-consuming, tedious, and prone to manual errors. Research on the implementation of computer aided diagnosis system (CADS) based on artificial intelligence (AI) to diagnose MS involves conventional machine learning and deep learning (DL) methods. In conventional machine learning, feature extraction, feature selection, and classification steps are carried out by using trial and error; on the contrary, these steps in DL are based on deep layers whose values are automatically learn. In this paper, a complete review of automated MS diagnosis methods performed using DL techniques with MRI neuroimaging modalities is provided. Initially, the steps involved in various CADS proposed using MRI modalities and DL techniques for MS diagnosis are investigated. The important preprocessing techniques employed in various works are analyzed. Most of the published papers on MS diagnosis using MRI modalities and DL are presented. The most significant challenges facing and future direction of automated diagnosis of MS using MRI modalities and DL techniques are also provided

Deteção automática de lesões de esclerose múltipla em imagens de ressonância magnética cerebral utilizando BIANCA

The aim of this work was to design and optimize a workflow to apply the Machine Learning classifier BIANCA (Brain Intensity AbNormalities Classification Algorithm) to detect lesions characterized by white matter T2 hyperintensity in clinical Magnetic Resonance Multiple Sclerosis datasets. The designed pipeline includes pre-processing, lesion identification and optimization of BIANCA options. The classifier has been trained and tuned on 15 cases making up the training dataset of the MICCAI 2016 (Medical Image Computing and Computer Assisted Interventions) challenge and then tested on 30 cases from the Lesjak et al. public dataset. The results obtained are in good agreement with those reported by the 13 teams concluding the MICCAI 2016 challenge, thus confirming that this algorithm can be a reliable tool to detect and classify Multiple Sclerosis lesions in Magnetic Resonance studies.Este trabalho teve como objetivo a conceção e otimização de um procedimento para aplicação de um algoritmo de Machine Learning, o classificador BIANCA (Brain Intensity AbNormalities Classification Algorithm), para deteção de lesões caracterizadas por hiperintensidade em T2 da matéria branca em estudos clínicos de Esclerose Múltipla por Ressonância Magnética. O procedimento concebido inclui pré-processamento, identificação das lesões e otimização dos parâmetros do algoritmo BIANCA. O classificador foi treinado e afinado utilizando os 15 casos clínicos que constituíam o conjunto de treino do desafio MICCAI 2016 (Medical Image Computing and Computer Assisted Interventions) e posteriormente testado em 30 casos clínicos de uma base de dados pública (Lesjak et al.). Os resultados obtidos são em concordância com os alcançados pelas 13 equipas que concluíram o desafio MICCAI 2016, confirmando que este algoritmo pode ser uma ferramenta válida para a deteção e classificação de lesões de Esclerose Múltipla em estudos de Ressonância Magnética.Mestrado em Tecnologias da Imagem Médic

A deep learning algorithm for white matter hyperintensity lesion detection and segmentation

Purpose: White matter hyperintensity (WMHI) lesions on MR images are an important indication of various types of brain diseases that involve inflammation and blood vessel abnormalities. Automated quantification of the WMHI can be valuable for the clinical management of patients, but existing automated software is often developed for a single type of disease and may not be applicable for clinical scans with thick slices and different scanning protocols. The purpose of the study is to develop and validate an algorithm for automatic quantification of white matter hyperintensity suitable for heterogeneous MRI data with different disease types. / Methods: We developed and evaluated “DeepWML”, a deep learning method for fully automated white matter lesion (WML) segmentation of multicentre FLAIR images. We used MRI from 507 patients, including three distinct white matter diseases, obtained in 9 centres, with a wide range of scanners and acquisition protocols. The automated delineation tool was evaluated through quantitative parameters of Dice similarity, sensitivity and precision compared to manual delineation (gold standard). / Results: The overall median Dice similarity coefficient was 0.78 (range 0.64 ~ 0.86) across the three disease types and multiple centres. The median sensitivity and precision were 0.84 (range 0.67 ~ 0.94) and 0.81 (range 0.64 ~ 0.92), respectively. The tool’s performance increased with larger lesion volumes. / Conclusion: DeepWML was successfully applied to a wide spectrum of MRI data in the three white matter disease types, which has the potential to improve the practical workflow of white matter lesion delineation

Improving the clinico-radiological association in neurological diseases

Despite the key role of magnetic resonance imaging (MRI) in the diagnosis and monitoring of multiple sclerosis (MS) and cerebral small vessel disease (SVD), the association between clinical and radiological disease manifestations is often only moderate, limiting the use of MRI-derived markers in the clinical routine or as endpoints in clinical trials. In the projects conducted as part of this thesis, we addressed this clinico-radiological gap using two different approaches. Lesion-symptom association: In two voxel-based lesion-symptom mapping studies, we aimed at strengthening lesion-symptom associations by identifying strategic lesion locations. Lesion mapping was performed in two large cohorts: a dataset of 2348 relapsing-remitting MS patients, and a population-based cohort of 1017 elderly subjects. T2-weighted lesion masks were anatomically aligned and a voxel-based statistical approach to relate lesion location to different clinical rating scales was implemented. In the MS lesion mapping, significant associations between white matter (WM) lesion location and several clinical scores were found in periventricular areas. Such lesion clusters appear to be associated with impairment of different physical and cognitive abilities, probably because they affect commissural and long projection fibers. In the SVD lesion mapping, the same WM fibers and the caudate nucleus were identified to significantly relate to the subjects’ cerebrovascular risk profiles, while no other locations were found to be associated with cognitive impairment. Atrophy-symptom association: With the construction of an anatomical physical phantom, we aimed at addressing reliability and robustness of atrophy-symptom associations through the provision of a “ground truth” for atrophy quantification. The built phantom prototype is composed of agar gels doped with MRI and computed tomography (CT) contrast agents, which realistically mimic T1 relaxation times of WM and grey matter (GM) and showing distinguishable attenuation coefficients using CT. Moreover, due to the design of anatomically simulated molds, both WM and GM are characterized by shapes comparable to the human counterpart. In a proof-of-principle study, the designed phantom was used to validate automatic brain tissue quantification by two popular software tools, where “ground truth” volumes were derived from high-resolution CT scans. In general, results from the same software yielded reliable and robust results across scans, while results across software were highly variable reaching volume differences of up to 8%

Multiple sclerosis cortical and WM lesion segmentation at 3T MRI: a deep learning method based on FLAIR and MP2RAGE.

The presence of cortical lesions in multiple sclerosis patients has emerged as an important biomarker of the disease. They appear in the earliest stages of the illness and have been shown to correlate with the severity of clinical symptoms. However, cortical lesions are hardly visible in conventional magnetic resonance imaging (MRI) at 3T, and thus their automated detection has been so far little explored. In this study, we propose a fully-convolutional deep learning approach, based on the 3D U-Net, for the automated segmentation of cortical and white matter lesions at 3T. For this purpose, we consider a clinically plausible MRI setting consisting of two MRI contrasts only: one conventional T2-weighted sequence (FLAIR), and one specialized T1-weighted sequence (MP2RAGE). We include 90 patients from two different centers with a total of 728 and 3856 gray and white matter lesions, respectively. We show that two reference methods developed for white matter lesion segmentation are inadequate to detect small cortical lesions, whereas our proposed framework is able to achieve a detection rate of 76% for both cortical and white matter lesions with a false positive rate of 29% in comparison to manual segmentation. Further results suggest that our framework generalizes well for both types of lesion in subjects acquired in two hospitals with different scanners