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

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Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains.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..

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

Unsupervised Domain Adaptation with Optimal Transport in multi-site segmentation of Multiple Sclerosis lesions from MRI data: Preprint

Automatic segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) images is essential for clinical assessment and treatment planning of MS. Recent years have seen an increasing use of Convolutional Neural Networks (CNNs) for this task. Although these methods provide accurate segmentation, their applicability in clinical settings remains limited due to a reproducibility issue across different image domains. MS images can have highly variable characteristics across patients, MRI scanners and imaging protocols; retraining a supervised model with data from each new domain is not a feasible solution because it requires manual annotation from expert radiologists. In this work, we explore an unsupervised solution to the problem of domain shift. We present a framework, Seg-JDOT, which adapts a deep model so that samples from a source domain and samples from a target domain sharing similar representations will be similarly segmented. We evaluated the framework on a multi-site dataset, MICCAI 2016, and showed that the adaptation towards a target site can bring remarkable improvements in a model performance over standard training

Dynamic low-level context for the detection of mild traumatic brain injury.

Mild traumatic brain injury (mTBI) appears as low contrast lesions in magnetic resonance (MR) imaging. Standard automated detection approaches cannot detect the subtle changes caused by the lesions. The use of context has become integral for the detection of low contrast objects in images. Context is any information that can be used for object detection but is not directly due to the physical appearance of an object in an image. In this paper, new low-level static and dynamic context features are proposed and integrated into a discriminative voxel-level classifier to improve the detection of mTBI lesions. Visual features, including multiple texture measures, are used to give an initial estimate of a lesion. From the initial estimate novel proximity and directional distance, contextual features are calculated and used as features for another classifier. This feature takes advantage of spatial information given by the initial lesion estimate using only the visual features. Dynamic context is captured by the proposed posterior marginal edge distance context feature, which measures the distance from a hard estimate of the lesion at a previous time point. The approach is validated on a temporal mTBI rat model dataset and shown to have improved dice score and convergence compared to other state-of-the-art approaches. Analysis of feature importance and versatility of the approach on other datasets are also provided

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Segmentation of Multiple Sclerosis Lesions Using Dictionary Learning in Feature Space

Manual segmentation is used in the diagnosis, management and evaluation of clinical trials for Multiple Sclerosis (MS), but human error makes manual segmentation variable. Automatic segmentation has been proposed using a Machine Learning algorithm Dictionary Learning (DL). We explored using different feature spaces to automatically segment MS lesions from healthy brain tissue. Methods of image texture analysis quantify the spatial distribution of the voxels in multi-weighted MR scans. We present the results of using a single voxel, single voxel and standard deviation (sigma) of adjacent voxels and a large spatial patch as feature spaces. The single voxel method segments the MS lesions with a Dice Similarity Coefficient (DSC) of 0.985 on simulated Brainweb data, but performed poorly with noise in the image (0.654). The single voxel and sigma performs at a DSC of 0.943 in the presence of 3% noise. The method should be attempted on real patient data