Longitudinal Brain Tumor Tracking, Tumor Grading, and Patient Survival Prediction Using MRI

This work aims to develop novel methods for brain tumor classification, longitudinal brain tumor tracking, and patient survival prediction. Consequently, this dissertation proposes three tasks. First, we develop a framework for brain tumor segmentation prediction in longitudinal multimodal magnetic resonance imaging (mMRI) scans, comprising two methods: feature fusion and joint label fusion (JLF). The first method fuses stochastic multi-resolution texture features with tumor cell density features, in order to obtain tumor segmentation predictions in follow-up scans from a baseline pre-operative timepoint. The second method utilizes JLF to combine segmentation labels obtained from (i) the stochastic texture feature-based and Random Forest (RF)-based tumor segmentation method; and (ii) another state-of-the-art tumor growth and segmentation method known as boosted Glioma Image Segmentation and Registration (GLISTRboost, or GB). With the advantages of feature fusion and label fusion, we achieve state-of-the-art brain tumor segmentation prediction. Second, we propose a deep neural network (DNN) learning-based method for brain tumor type and subtype grading using phenotypic and genotypic data, following the World Health Organization (WHO) criteria. In addition, the classification method integrates a cellularity feature which is derived from the morphology of a pathology image to improve classification performance. The proposed method achieves state-of-the-art performance for tumor grading following the new CNS tumor grading criteria. Finally, we investigate brain tumor volume segmentation, tumor subtype classification, and overall patient survival prediction, and then we propose a new context- aware deep learning method, known as the Context Aware Convolutional Neural Network (CANet). Using the proposed method, we participated in the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) for brain tumor volume segmentation and overall survival prediction tasks. In addition, we also participated in the Radiology-Pathology Challenge 2019 (CPM-RadPath 2019) for Brain Tumor Subtype Classification, organized by the Medical Image Computing & Computer Assisted Intervention (MICCAI) Society. The online evaluation results show that the proposed methods offer competitive performance from their use of state-of-the-art methods in tumor volume segmentation, promising performance on overall survival prediction, and state-of-the-art performance on tumor subtype classification. Moreover, our result was ranked second place in the testing phase of the CPM-RadPath 2019

Context Aware Deep Learning for Brain Tumor Segmentation, Subtype Classification, and Survival Prediction Using Radiology Images

A brain tumor is an uncontrolled growth of cancerous cells in the brain. Accurate segmentation and classification of tumors are critical for subsequent prognosis and treatment planning. This work proposes context aware deep learning for brain tumor segmentation, subtype classification, and overall survival prediction using structural multimodal magnetic resonance images (mMRI). We first propose a 3D context aware deep learning, that considers uncertainty of tumor location in the radiology mMRI image sub-regions, to obtain tumor segmentation. We then apply a regular 3D convolutional neural network (CNN) on the tumor segments to achieve tumor subtype classification. Finally, we perform survival prediction using a hybrid method of deep learning and machine learning. To evaluate the performance, we apply the proposed methods to the Multimodal Brain Tumor Segmentation Challenge 2019 (BraTS 2019) dataset for tumor segmentation and overall survival prediction, and to the dataset of the Computational Precision Medicine Radiology-Pathology (CPM-RadPath) Challenge on Brain Tumor Classification 2019 for tumor classification. We also perform an extensive performance evaluation based on popular evaluation metrics, such as Dice score coefficient, Hausdorff distance at percentile 95 (HD95), classification accuracy, and mean square error. The results suggest that the proposed method offers robust tumor segmentation and survival prediction, respectively. Furthermore, the tumor classification results in this work is ranked at second place in the testing phase of the 2019 CPM-RadPath global challenge

Deep Learning with Context Encoding for Semantic Brain Tumor Segmentation and Patient Survival Prediction

One of the most challenging problems encountered in deep learning-based brain tumor segmentation models is the misclassification of tumor tissue classes due to the inherent imbalance in the class representation. Consequently, strong regularization methods are typically considered when training large-scale deep learning models for brain tumor segmentation to overcome undue bias towards representative tissue types. However, these regularization methods tend to be computationally exhaustive, and may not guarantee the learning of features representing all tumor tissue types that exist in the input MRI examples. Recent work in context encoding with deep CNN models have shown promise for semantic segmentation of natural scenes, with particular improvements in small object segmentation due to improved representative feature learning. Accordingly, we propose a novel, efficient 3DCNN based deep learning framework with context encoding for semantic brain tumor segmentation using multimodal magnetic resonance imaging (mMRI). The context encoding module in the proposed model enforces rich, class-dependent feature learning to improve the overall multi-label segmentation performance. We subsequently utilize context augmented features in a machine-learning based survival prediction pipeline to improve the prediction performance. The proposed method is evaluated using the publicly available 2019 Brain Tumor Segmentation (BraTS) and survival prediction challenge dataset. The results show that the proposed method significantly improves the tumor tissue segmentation performance and the overall survival prediction performance

Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

This paper introduces a novel methodology to integrate human brain connectomics and parcellation for brain tumor segmentation and survival prediction. For segmentation, we utilize an existing brain parcellation atlas in the MNI152 1mm space and map this parcellation to each individual subject data. We use deep neural network architectures together with hard negative mining to achieve the final voxel level classification. For survival prediction, we present a new method for combining features from connectomics data, brain parcellation information, and the brain tumor mask. We leverage the average connectome information from the Human Connectome Project and map each subject brain volume onto this common connectome space. From this, we compute tractographic features that describe potential neural disruptions due to the brain tumor. These features are then used to predict the overall survival of the subjects. The main novelty in the proposed methods is the use of normalized brain parcellation data and tractography data from the human connectome project for analyzing MR images for segmentation and survival prediction. Experimental results are reported on the BraTS2018 dataset.Comment: 14 pages, 5 figures, 4 tables, accepted by BrainLes 2018 MICCAI worksho

Feature-Guided Deep Radiomics for Glioblastoma Patient Survival Prediction

Glioblastoma is recognized as World Health Organization (WHO) grade IV glioma with an aggressive growth pattern. The current clinical practice in diagnosis and prognosis of Glioblastoma using MRI involves multiple steps including manual tumor sizing. Accurate identification and segmentation of multiple abnormal tissues within tumor volume in MRI is essential for precise survival prediction. Manual tumor and abnormal tissue detection and sizing are tedious, and subject to inter-observer variability. Consequently, this work proposes a fully automated MRI-based glioblastoma and abnormal tissue segmentation, and survival prediction framework. The framework includes radiomics feature-guided deep neural network methods for tumor tissue segmentation; followed by survival regression and classification using these abnormal tumor tissue segments and other relevant clinical features. The proposed multiple abnormal tumor tissue segmentation step effectively fuses feature-based and feature-guided deep radiomics information in structural MRI. The survival prediction step includes two representative survival prediction pipelines that combine different feature selection and regression approaches. The framework is evaluated using two recent widely used benchmark datasets from Brain Tumor Segmentation (BraTS) global challenges in 2017 and 2018. The best overall survival pipeline in the proposed framework achieves leave-one-out cross-validation (LOOCV) accuracy of 0.73 for training datasets and 0.68 for validation datasets, respectively. These training and validation accuracies for tumor patient survival prediction are among the highest reported in literature. Finally, a critical analysis of radiomics features and efficacy of these features in segmentation and survival prediction performance is presented as lessons learned