A review on a deep learning perspective in brain cancer classification

AWorld Health Organization (WHO) Feb 2018 report has recently shown that mortality rate due to brain or central nervous system (CNS) cancer is the highest in the Asian continent. It is of critical importance that cancer be detected earlier so that many of these lives can be saved. Cancer grading is an important aspect for targeted therapy. As cancer diagnosis is highly invasive, time consuming and expensive, there is an immediate requirement to develop a non-invasive, cost-effective and efficient tools for brain cancer characterization and grade estimation. Brain scans using magnetic resonance imaging (MRI), computed tomography (CT), as well as other imaging modalities, are fast and safer methods for tumor detection. In this paper, we tried to summarize the pathophysiology of brain cancer, imaging modalities of brain cancer and automatic computer assisted methods for brain cancer characterization in a machine and deep learning paradigm. Another objective of this paper is to find the current issues in existing engineering methods and also project a future paradigm. Further, we have highlighted the relationship between brain cancer and other brain disorders like stroke, Alzheimer’s, Parkinson’s, andWilson’s disease, leukoriaosis, and other neurological disorders in the context of machine learning and the deep learning paradigm

White Matter Hyperintensity and Multi-region Brain MRI Segmentation Using Convolutional Neural Network

Accurate segmentation of WMH (white matter hyperintensity) from the magnetic resonance image is a prerequisite for many precise medical procedures, especially for the diagnosis of vascular dementia. Brain segmentation has important research significance and clinical application prospects especially for early detection of Alzheimer’s disease. In order to effectively perform accurate segmentation according to the MRI characteristics of different regions of the brain, this thesis proposed an optimized 3D u-net and used WHM segmentation as a pre-experiment to select the good hyperparameters (i.e. network depth, image fusion method, and the implementation of loss function) to construct an image feature learning network with both long and short skip connections. Soft voting is used as the postprocessing procedure. Our model is evaluated by a 10-fold cross-validation and achieved a dice score of 0.78 for binary segmentation (WMH segmentation) and accuracy of 0.96 for multi-class segmentation (139 regions brain segmentation), outperforming other methods

Cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy in an Israeli family

Cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) is the most common monogenic form of hereditary cerebral microangiopathy, and is caused by over 170 different mutations in the NOTCH3 gene at locus 19p13.1–13.26. We report the first study of familial CADASIL in a 39-year-old Jewish woman and her mother who had died previously. The patient’s investigations revealed a normal hemogram with no vascular risk factors or chronic disease. Lumbar puncture was normal. Cranial computed tomography scan revealed bilateral diffuse hypodensities in the subcortical white matter. Cranial magnetic resonance imaging showed hyperintense lesions in the cerebral white matter on T2-weighted images. On electron microscopy, a characteristic granular osmiophilic material was seen in the basement membrane surrounding the pericytes and smooth muscle cells in small-sized and medium-sized vessels. Molecular analysis of the NOTCH3 gene was performed with automatic sequencing of exon 3 and 4 (and intron-exon boundaries) showing a nucleotide c.268C > T substitution, leading to a pathogenic amino acid substitution of p.Arg90Cys, confirming a diagnosis of CADASIL. This mutation was also found in the patient’s mother. Although the exact prevalence of CADASIL is not known, this disorder has been reported worldwide, and now including Jews, with a genotype and clinical phenotype similar to that in other ethnic groups

DEEP-AD: The deep learning model for diagnostic classification and prognostic prediction of alzheimer's disease

In terms of context, the aim of this dissertation is to aid neuroradiologists in their clinical judgment regarding the early detection of AD by using DL. To that aim, the system design research methodology is suggested in this dissertation for achieving three goals. The first goal is to investigate the DL models that have performed well at identifying patterns associated with AD, as well as the accuracy so far attained, limitations, and gaps. A systematic review of the literature (SLR) revealed a shortage of empirical studies on the early identification of AD through DL. In this regard, thirteen empirical studies were identified and examined. We concluded that three-dimensional (3D) DL models have been generated far less often and that their performance is also inadequate to qualify them for clinical trials. The second goal is to provide the neuroradiologist with the computer-interpretable information they need to analyze neuroimaging biomarkers. Given this context, the next step in this dissertation is to find the optimum DL model to analyze neuroimaging biomarkers. It has been achieved in two steps. In the first step, eight state-of-the-art DL models have been implemented by training from scratch using end-to-end learning (E2EL) for two binary classification tasks (AD vs. CN and AD vs. stable MCI) and compared by utilizing MRI scans from the publicly accessible datasets of neuroimaging biomarkers. Comparative analysis is carried out by utilizing efficiency-effects graphs, comprehensive indicators, and ranking mechanisms. For the training of the AD vs. sMCI task, the EfficientNet-B0 model gets the highest value for the comprehensive indicator and has the fewest parameters. DenseNet264 performed better than the others in terms of evaluation matrices, but since it has the most parameters, it costs more to train. For the AD vs. CN task by DenseNet264, we achieved 100% accuracy for training and 99.56% accuracy for testing. However, the classification accuracy was still only 82.5% for the AD vs. sMCI task. In the second step, fusion of transfer learning (TL) with E2EL is applied to train the EfficientNet-B0 for the AD vs. sMCI task, which achieved 95.29% accuracy for training and 93.10% accuracy for testing. Additionally, we have also implemented EfficientNet-B0 for the multiclass AD vs. CN vs. sMCI classification task with E2EL to be used in ensemble of models and achieved 85.66% training accuracy and 87.38% testing accuracy. To evaluate the model’s robustness, neuroradiologists must validate the implemented model. As a result, the third goal of this dissertation is to create a tool that neuroradiologists may use at their convenience. To achieve this objective, this dissertation proposes a web-based application (DEEP-AD) that has been created by making an ensemble of Efficient-Net B0 and DenseNet 264 (based on the contribution of goal 2). The accuracy of a DEEP-AD prototype has undergone repeated evaluation and improvement. First, we validated 41 subjects of Spanish MRI datasets (acquired from HT Medica, Madrid, Spain), achieving an accuracy of 82.90%, which was later verified by neuroradiologists. The results of these evaluation studies showed the accomplishment of such goals and relevant directions for future research in applied DL for the early detection of AD in clinical settings.En términos de contexto, el objetivo de esta tesis es ayudar a los neurorradiólogos en su juicio clínico sobre la detección precoz de la AD mediante el uso de DL. Para ello, en esta tesis se propone la metodología de investigación de diseño de sistemas para lograr tres objetivos. El segundo objetivo es proporcionar al neurorradiólogo la información interpretable por ordenador que necesita para analizar los biomarcadores de neuroimagen. Dado este contexto, el siguiente paso en esta tesis es encontrar el modelo DL óptimo para analizar biomarcadores de neuroimagen. Esto se ha logrado en dos pasos. En el primer paso, se han implementado ocho modelos DL de última generación mediante entrenamiento desde cero utilizando aprendizaje de extremo a extremo (E2EL) para dos tareas de clasificación binarias (AD vs. CN y AD vs. MCI estable) y se han comparado utilizando escaneos MRI de los conjuntos de datos de biomarcadores de neuroimagen de acceso público. El análisis comparativo se lleva a cabo utilizando gráficos de efecto-eficacia, indicadores exhaustivos y mecanismos de clasificación. Para el entrenamiento de la tarea AD vs. sMCI, el modelo EfficientNet-B0 obtiene el valor más alto para el indicador exhaustivo y tiene el menor número de parámetros. DenseNet264 obtuvo mejores resultados que los demás en términos de matrices de evaluación, pero al ser el que tiene más parámetros, su entrenamiento es más costoso. Para la tarea AD vs. CN de DenseNet264, conseguimos una accuracy del 100% en el entrenamiento y del 99,56% en las pruebas. Sin embargo, la accuracy de la clasificación fue sólo del 82,5% para la tarea AD vs. sMCI. En el segundo paso, se aplica la fusión del aprendizaje por transferencia (TL) con E2EL para entrenar la EfficientNet-B0 para la tarea AD vs. sMCI, que alcanzó una accuracy del 95,29% en el entrenamiento y del 93,10% en las pruebas. Además, también hemos implementado EfficientNet-B0 para la tarea de clasificación multiclase AD vs. CN vs. sMCI con E2EL para su uso en conjuntos de modelos y hemos obtenido una accuracy de entrenamiento del 85,66% y una precisión de prueba del 87,38%. Para evaluar la solidez del modelo, los neurorradiólogos deben validar el modelo implementado. Como resultado, el tercer objetivo de esta disertación es crear una herramienta que los neurorradiólogos puedan utilizar a su conveniencia. Para lograr este objetivo, esta disertación propone una aplicación basada en web (DEEP-AD) que ha sido creada haciendo un ensemble de Efficient-Net B0 y DenseNet 264 (basado en la contribución del objetivo 2). La accuracy del prototipo DEEP-AD ha sido sometida a repetidas evaluaciones y mejoras. En primer lugar, validamos 41 sujetos de conjuntos de datos de MRI españoles (adquiridos de HT Medica, Madrid, España), logrando una accuracy del 82,90%, que posteriormente fue verificada por neurorradiólogos. Los resultados de estos estudios de evaluación mostraron el cumplimiento de dichos objetivos y las direcciones relevantes para futuras investigaciones en DL, aplicada en la detección precoz de la AD en entornos clínicos.Escuela de DoctoradoDoctorado en Tecnologías de la Información y las Telecomunicacione

THE ROLE OF DNA METHYLATION IN WHITE MATTER HYPERINTENSITY BURDEN: AN INTEGRATIVE APPROACH

Cerebral white matter hyperintensities (WMH) on MRI are an indicator of cerebral small vessel disease, a major risk factor for vascular dementia and stroke. DNA methylation may contribute to the molecular underpinnings of WMH, which are highly heritable. We performed a meta-analysis of 11 epigenome-wide association studies in 6,019 middle-aged to elderly subjects, who were free of dementia and stroke and were of African (AA) or European (EA) descent. In each study, association between WMH volume and each CpG was tested within ancestry using a linear mixed model, adjusted for age, sex, total intracranial volume, white blood cell count, technical covariates, BMI, smoking and blood pressure (BP). To detect differentially methylated regions (DMRs), we also calculated region-based p values accounting for spatial correlations among CpGs. No individual CpG reached epigenome-wide significance, but suggestive novel associations were identified with cg17577122 (CLDN5, P=2.39E-7), cg24202936 (LOC441601, P=3.78E-7), cg03116124 (TRIM67, P=6.55E-7), cg04245766 (BMP4, P=3.78E-7) and cg06809326 (CCDC144NL, P=6.14E-7). Gene enrichment analyses implicated pathways involved in regulation of cell development and differentiation, especially of endothelial cells. We identified 11 DMRs (PSidak\u3c0.05) and two were mapped to BP-related genes (HIVEP3, TCEA2). The most significant DMRs were mapped to PRMT1, a protein arginine methyltransferase involved in glioblastomagenesis (P=7.9E-12), and mapped to CCDC144NL-AS1, an antisense transcript of CCDC144NL (P=1.6E-11). Genes mapping to DMRs were enriched in biological processes related to lipoprotein metabolism and transport. Bi-directional Mendelian randomization analysis showed that DNA methylation level at cg06809326 influenced WMH burden (OR [95% CI] = 1.7[1.2-2.5], P=0.001) but not the reverse (P=0.89). Additionally, increased methylation at cg06809326 was associated with lower expression of CCDC144NL (P=3.3E-2), and two-step Mendelian randomization analysis supported its mediating role in the association of cg06809326 and WMH burden. CCDC144NL is known to be associated with diabetic retinopathy and the coiled coil proteins in general promotes integrin-dependent cell adhesion. Integrin-related pathway was further supported by integrative genetic analyses. In conclusion, we identified novel epigenetic loci associated with WMH burden, and further supported the role of cg06809326 in the WMH etiology implicating integrin-mediated pathology

REGISTRATION AND SEGMENTATION OF BRAIN MR IMAGES FROM ELDERLY INDIVIDUALS

Quantitative analysis of the MRI structural and functional images is a fundamental component in the assessment of brain anatomical abnormalities, in mapping functional activation onto human anatomy, in longitudinal evaluation of disease progression, and in computer-assisted neurosurgery or surgical planning. Image registration and segmentation is central in analyzing structural and functional MR brain images. However, due to increased variability in brain morphology and age-related atrophy, traditional methods for image registration and segmentation are not suitable for analyzing MR brain images from elderly individuals. The overall goal of this dissertation is to develop algorithms to improve the registration and segmentation accuracy in the geriatric population. The specific aims of this work includes 1) to implement a fully deformable registration pipeline to allow a higher degree of spatial deformation and produce more accurate deformation field, 2) to propose and validate an optimum template selection method for atlas-based segmentation, 3) to propose and validate a multi-template strategy for image normalization, which characterizes brain structural variations in the elderly, 4) to develop an automated segmentation and localization method to access white matter integrity (WMH) in the elderly population, and finally 5) to study the default-mode network (DMN) connectivity and white matter hyperintensity in late-life depression (LLD) with the developed registration and segmentation methods. Through a series of experiments, we have shown that the deformable registration pipeline and the template selection strategies lead to improved accuracy in the brain MR image registration and segmentation, and the automated WMH segmentation and localization method provides more specific and more accurate information about volume and spatial distribution of WMH than traditional visual grading methods. Using the developed methods, our clinical study provides evidence for altered DMN connectivity in LLD. The correlation between WMH volume and DMN connectivity emphasizes the role of vascular changes in LLD's etiopathogenesis

On the computational assessment of white matter hyperintensity progression: difficulties in method selection and bias field correction performance on images with significant white matter pathology

Introduction Subtle inhomogeneities in the scanner’s magnetic fields (B0 and B1) alter the intensity levels of the structural magnetic resonance imaging (MRI) affecting the volumetric assessment of WMH changes. Here, we investigate the influence that (1) correcting the images for the B1 inhomogeneities (i.e. bias field correction (BFC)) and (2) selection of the WMH change assessment method can have on longitudinal analyses of WMH progression and discuss possible solutions. Methods We used brain structural MRI from 46 mild stroke patients scanned at stroke onset and 3 years later. We tested three BFC approaches: FSL-FAST, N4 and exponentially entropy-driven homomorphic unsharp masking (E2D-HUM) and analysed their effect on the measured WMH change. Separately, we tested two methods to assess WMH changes: measuring WMH volumes independently at both time points semi-automatically (MCMxxxVI) and subtracting intensity-normalised FLAIR images at both time points following image gamma correction. We then combined the BFC with the computational method that performed best across the whole sample to assess WMH changes. Results Analysis of the difference in the variance-to-mean intensity ratio in normal tissue between BFC and uncorrected images and visual inspection showed that all BFC methods altered the WMH appearance and distribution, but FSL-FAST in general performed more consistently across the sample and MRI modalities. The WMH volume change over 3 years obtained with MCMxxxVI with vs. without FSL-FAST BFC did not significantly differ (medians(IQR)(with BFC) = 3.2(6.3) vs. 2.9(7.4)ml (without BFC), p = 0.5), but both differed significantly from the WMH volume change obtained from subtracting post-processed FLAIR images (without BFC)(7.6(8.2)ml, p < 0.001). This latter method considerably inflated the WMH volume change as subtle WMH at baseline that became more intense at follow-up were counted as increase in the volumetric change. Conclusions Measurement of WMH volume change remains challenging. Although the overall volumetric change was not significantly affected by the application of BFC, these methods distorted the image intensity distribution affecting subtle WMH. Subtracting the FLAIR images at both time points following gamma correction seems a promising technique but is adversely affected by subtle WMH. It is important to take into account not only the changes in volume but also in the signal intensity