Supervised learning based multimodal MRI brain tumour segmentation using texture features from supervoxels

BACKGROUND: Accurate segmentation of brain tumour in magnetic resonance images (MRI) is a difficult task due to various tumour types. Using information and features from multimodal MRI including structural MRI and isotropic (p) and anisotropic (q) components derived from the diffusion tensor imaging (DTI) may result in a more accurate analysis of brain images. METHODS: We propose a novel 3D supervoxel based learning method for segmentation of tumour in multimodal MRI brain images (conventional MRI and DTI). Supervoxels are generated using the information across the multimodal MRI dataset. For each supervoxel, a variety of features including histograms of texton descriptor, calculated using a set of Gabor filters with different sizes and orientations, and first order intensity statistical features are extracted. Those features are fed into a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. RESULTS: The method is evaluated on two datasets: 1) Our clinical dataset: 11 multimodal images of patients and 2) BRATS 2013 clinical dataset: 30 multimodal images. For our clinical dataset, the average detection sensitivity of tumour (including tumour core and oedema) using multimodal MRI is 86% with balanced error rate (BER) 7%; while the Dice score for automatic tumour segmentation against ground truth is 0.84. The corresponding results of the BRATS 2013 dataset are 96%, 2% and 0.89, respectively. CONCLUSION: The method demonstrates promising results in the segmentation of brain tumour. Adding features from multimodal MRI images can largely increase the segmentation accuracy. The method provides a close match to expert delineation across all tumour grades, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management

3D Convolutional Neural Networks for Tumor Segmentation using Long-range 2D Context

We present an efficient deep learning approach for the challenging task of tumor segmentation in multisequence MR images. In recent years, Convolutional Neural Networks (CNN) have achieved state-of-the-art performances in a large variety of recognition tasks in medical imaging. Because of the considerable computational cost of CNNs, large volumes such as MRI are typically processed by subvolumes, for instance slices (axial, coronal, sagittal) or small 3D patches. In this paper we introduce a CNN-based model which efficiently combines the advantages of the short-range 3D context and the long-range 2D context. To overcome the limitations of specific choices of neural network architectures, we also propose to merge outputs of several cascaded 2D-3D models by a voxelwise voting strategy. Furthermore, we propose a network architecture in which the different MR sequences are processed by separate subnetworks in order to be more robust to the problem of missing MR sequences. Finally, a simple and efficient algorithm for training large CNN models is introduced. We evaluate our method on the public benchmark of the BRATS 2017 challenge on the task of multiclass segmentation of malignant brain tumors. Our method achieves good performances and produces accurate segmentations with median Dice scores of 0.918 (whole tumor), 0.883 (tumor core) and 0.854 (enhancing core). Our approach can be naturally applied to various tasks involving segmentation of lesions or organs.Comment: Submitted to the journal Computerized Medical Imaging and Graphic

AUTOMATIC BRAIN TUMOR SEGMENTATION WITH K-MEANS, FUZZY C-MEANS, SELF-ORGANIZING MAP AND OTSU METHODS

AutomatIc BraIn Tumor SegmentatIon wIth K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu MethodsAbstractThe human brain is an amazing organ of the human nervous system and controls all functions of our body. Brain tumors emerge from a mass of abnormal cells in the brain, and catching tumors early often allows for more treatment options. For diagnosing brain tumors, it has been benefited mostly from magnetic resonance images. In this study, we have developed the segmentation systems using the methods as K-Means, Fuzzy C-Means, Self-Organizing Map, Otsu, and the hybrid method of them, and evaluated the methods according to their success rates of segmentation. The developed systems, which take the brain image of MRI as input, perform skull stripping, preprocessing, and segmentation is performed using the clustering algorithms as K-Means, Fuzzy C-Means, Self-Organizing Map and Otsu Methods. Before preprocessing, the skull region is removed from the images in the MRI brain image data set. In preprocessing, the quality of the brain images is enhanced and the noise of the images is removed by some various filtering and morphological techniques. Finally, with the clustering and thresholding techniques, the tumor area of the brain is detected, and then the systems of the segmentation have been evaluated and compared with each other according to accuracy, true positive rate, and true negative rate.Keywords: Brain Tumor Segmentation, Medical Imaging, Fuzzy C-Means, K-Means, Self-Organizing Map, Otsu MethodBulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemelİ Ağ VE Otsu Metot İLE BEYİN TÜMÖRÜ SEGMENTASYONU Özetİnsan beyni, insan sinir sisteminin en önemli organıdır ve vücudumuzun tamamını kontrol eder. Beyin tümörleri beyindeki normal olmayan hücrelerden oluşur ve tümörleri erken tespit etmek birçok tedavi seçeneklerinin uygulanmasına olanak sağlar. Beyin tümörlerinin teşhisi için çoğunlukla manyetik rezonans görüntülerinden yararlanılmıştır. Bu çalışmada, Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu Metot ve bu metotların birleşiminden oluşan hibrid metotlar kullanılarak beyin tümör segmentasyon sistemleri geliştirilmiştir. Bu metotların segmentasyon başarı oranları tespit edilmiş ve birbirleriyle karşılaştırılmıştır. Geliştirilen sistemlerde, ilk olarak MRI beyin görüntülerini girdi olarak alınır, sonra kafatası bölgesinin görüntüden ayrılması, önişleme ve Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu metot gibi algoritmalarla segmentasyon işlemleri uygulanır. Önişlemden önce, kafatası bölgesi, MRI beyin görüntüsü veri setindeki görüntülerden çıkarılır. Ön işlemede, beyin görüntülerinin kalitesi iyileştirilir ve görüntülerin gürültüsü, çeşitli filtreleme ve morfolojik tekniklerle kaldırılır. Son olarak, kümeleme ve eşikleme teknikleri ile beynin tümör bölgesi tespit edildi. Daha sonra, segmentasyon sistemleri değerlendirildi ve doğruluk, gerçek pozitif oranı ve gerçek negatif oranına göre birbirleriyle karşılaştırıldı.Anahtar Kelimeler: Beyin Tümörü Segmentasyonu, Tıbbi Görüntüleme, Bulanık C-Ortalamalar, K-Ortalamalar, Özdüzenlemeli Ağ, Otsu Meto

MRI Brain Tumor Segmentation and Patient Survival Prediction Using Random Forests and Fully Convolutional Networks

In this paper, we propose a learning based method for automated segmentation of brain tumor in multimodal MRI images, which incorporates two sets of machine-learned and hand-crafted features. Fully convolutional networks (FCN) forms the machine-learned features and texton based histograms are considered as hand-crafted features. Random forest (RF) is used to classify the MRI image voxels into normal brain tissues and different parts of tumors. The volumetric features from the segmented tumor tissues and patient age applying to an RF is used to predict the survival time. The method was evaluated on MICCAI-BRATS 2017 challenge dataset. The mean Dice overlap measures for segmentation of validation dataset are 0.86, 0.78 and 0.66 for whole tumor, core and enhancing tumor, respectively. The validation Hausdorff values are 7.61, 8.70 and 3.76. For the survival prediction task, the classification accuracy, pairwise mean square error and Spearman rank are 0.485, 198749 and 0.334, respectively

SEGMA: an automatic SEGMentation Approach for human brain MRI using sliding window and random forests

Quantitative volumes from brain magnetic resonance imaging (MRI) acquired across the life course may be useful for investigating long term effects of risk and resilience factors for brain development and healthy aging, and for understanding early life determinants of adult brain structure. Therefore, there is an increasing need for automated segmentation tools that can be applied to images acquired at different life stages. We developed an automatic segmentation method for human brain MRI, where a sliding window approach and a multi-class random forest classifier were applied to high-dimensional feature vectors for accurate segmentation. The method performed well on brain MRI data acquired from 179 individuals, analyzed in three age groups: newborns (38–42 weeks gestational age), children and adolescents (4–17 years) and adults (35–71 years). As the method can learn from partially labeled datasets, it can be used to segment large-scale datasets efficiently. It could also be applied to different populations and imaging modalities across the life course

Prediction of glioma‑subtypes: comparison of performance on a DL classifier using bounding box areas versus annotated tumors

Background: For brain tumors, identifying the molecular subtypes from magnetic resonance imaging (MRI) isdesirable, but remains a challenging task. Recent machine learning and deep learning (DL) approaches may help theclassification/prediction of tumor subtypes through MRIs. However, most of these methods require annotated datawith ground truth (GT) tumor areas manually drawn by medical experts. The manual annotation is a time consumingprocess with high demand on medical personnel. As an alternative automatic segmentation is often used. However, itdoes not guarantee the quality and could lead to improper or failed segmented boundaries due to differences in MRIacquisition parameters across imaging centers, as segmentation is an ill‑defined problem. Analogous to visual objecttracking and classification, this paper shifts the paradigm by training a classifier using tumor bounding box areas inMR images. The aim of our study is to see whether it is possible to replace GT tumor areas by tumor bounding boxareas (e.g. ellipse shaped boxes) for classification without a significant drop in performance.Method: In patients with diffuse gliomas, training a deep learning classifier for subtype prediction by employ‑ing tumor regions of interest (ROIs) using ellipse bounding box versus manual annotated data. Experiments wereconducted on two datasets (US and TCGA) consisting of multi‑modality MRI scans where the US dataset containedpatients with diffuse low‑grade gliomas (dLGG) exclusively.Results: Prediction rates were obtained on 2 test datasets: 69.86% for 1p/19q codeletion status on US dataset and79.50% for IDH mutation/wild‑type on TCGA dataset. Comparisons with that of using annotated GT tumor data fortraining showed an average of 3.0% degradation (2.92% for 1p/19q codeletion status and 3.23% for IDH genotype).Conclusion: Using tumor ROIs, i.e., ellipse bounding box tumor areas to replace annotated GT tumor areas for train‑ing a deep learning scheme, cause only a modest decline in performance in terms of subtype prediction. With moredata that can be made available, this may be a reasonable trade‑off where decline in performance may be counter‑acted with more data