Supervised learning-based multimodal MRI brain image analysis

Medical imaging plays an important role in clinical procedures related to cancer, such as diagnosis, treatment selection, and therapy response evaluation. Magnetic resonance imaging (MRI) is one of the most popular acquisition modalities which is widely used in brain tumour analysis and can be acquired with different acquisition protocols, e.g. conventional and advanced. Automated segmentation of brain tumours in MR images is a difficult task due to their high variation in size, shape and appearance. Although many studies have been conducted, it still remains a challenging task and improving accuracy of tumour segmentation is an ongoing field. The aim of this thesis is to develop a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from multimodal MRI images. In this thesis, firstly, the whole brain tumour is segmented from fluid attenuated inversion recovery (FLAIR) MRI, which is commonly acquired in clinics. The segmentation is achieved using region-wise classification, in which regions are derived from superpixels. Several image features including intensity-based, Gabor textons, fractal analysis and curvatures are calculated from each superpixel within the entire brain area in FLAIR MRI to ensure a robust classification. Extremely randomised trees (ERT) classifies each superpixel into tumour and non-tumour. Secondly, the method is extended to 3D supervoxel based learning for segmentation and classification of tumour tissue subtypes in multimodal MRI brain images. Supervoxels are generated using the information across the multimodal MRI data set. This is then followed by a random forests (RF) classifier to classify each supervoxel into tumour core, oedema or healthy brain tissue. The information from the advanced protocols of diffusion tensor imaging (DTI), i.e. isotropic (p) and anisotropic (q) components is also incorporated to the conventional MRI to improve segmentation accuracy. Thirdly, to further improve the segmentation of tumour tissue subtypes, the machine-learned features from fully convolutional neural network (FCN) are investigated and combined with hand-designed texton features to encode global information and local dependencies into feature representation. The score map with pixel-wise predictions is used as a feature map which is learned from multimodal MRI training dataset using the FCN. The machine-learned features, along with hand-designed texton features are then applied to random forests to classify each MRI image voxel into normal brain tissues and different parts of tumour. The methods are evaluated on two datasets: 1) clinical dataset, and 2) publicly available Multimodal Brain Tumour Image Segmentation Benchmark (BRATS) 2013 and 2017 dataset. The experimental results demonstrate the high detection and segmentation performance of the III single modal (FLAIR) method. The average detection sensitivity, balanced error rate (BER) and the Dice overlap measure for the segmented tumour against the ground truth for the clinical data are 89.48%, 6% and 0.91, respectively; whilst, for the BRATS dataset, the corresponding evaluation results are 88.09%, 6% and 0.88, respectively. The corresponding results for the tumour (including tumour core and oedema) in the case of multimodal MRI method are 86%, 7%, 0.84, for the clinical dataset and 96%, 2% and 0.89 for the BRATS 2013 dataset. The results of the FCN based method show that the application of the RF classifier to multimodal MRI images using machine-learned features based on FCN and hand-designed features based on textons provides promising segmentations. The Dice overlap measure for automatic brain tumor segmentation against ground truth for the BRATS 2013 dataset is 0.88, 0.80 and 0.73 for complete tumor, core and enhancing tumor, respectively, which is competitive to the state-of-the-art methods. The corresponding results for BRATS 2017 dataset are 0.86, 0.78 and 0.66 respectively. The methods demonstrate promising results in the segmentation of brain tumours. This provides a close match to expert delineation across all grades of glioma, leading to a faster and more reproducible method of brain tumour detection and delineation to aid patient management. In the experiments, texton has demonstrated its advantages of providing significant information to distinguish various patterns in both 2D and 3D spaces. The segmentation accuracy has also been largely increased by fusing information from multimodal MRI images. Moreover, a unified framework is present which complementarily integrates hand-designed features with machine-learned features to produce more accurate segmentation. The hand-designed features from shallow network (with designable filters) encode the prior-knowledge and context while the machine-learned features from a deep network (with trainable filters) learn the intrinsic features. Both global and local information are combined using these two types of networks that improve the segmentation accuracy

An image analysis approach to MRI brain tumour grading

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Brain tumour grading in different MRI protocols using SVM on statistical features

In this paper a feasibility study of brain MRI dataset classification, using ROIs which have been segmented either manually or through a superpixel based method in conjunction with statistical pattern recognition methods is presented. In our study, 471 extracted ROIs from 21 Brain MRI datasets are used, in order to establish which features distinguish better between three grading classes. Thirty-eight statistical measurements were collected from the ROIs. We found by using the Leave-One-Out method that the combination of the features from the 1st and 2nd order statistics, achieved high classification accuracy in pair-wise grading comparisons

Evaluation of the Impact of Courses on Islamic Education and Religious Concepts on the Promotion of Medical Ethics: A Case Study on the Students of Kerman University of Medical Sciences

Background: According to the status of ethics in medical education and owing to the students’ talent and rational spirit, it seems that the education system, despite the emphasis on courses such as medical ethics, should focus on the improvement of teaching quality of Islamic education and explanation of religious concepts, since emphasis on increasing the quality of Islamic education leads to the promotion of medical ethics. Objectives: The current study aimed at evaluating the impact of courses on Islamic education and religious concepts on the promotion of medical ethics among the students of Kerman University of Medical Sciences, Kerman, Iran. Methods: The current descriptive cross sectional study was conducted on 5831 students of Kerman University of Medical Sciences as the statistical population in the academic year of 2016 - 2017. Using the Morgan table, 360 subjects were selected as the study sample using stratified random sampling method. In order to collect data, a standard questionnaire, which its reliability was confirmed by Cronbach’s alpha coefficient, was used. Data were analyzed using structural equation modeling with AMOS software. Results: Structural equation modeling was used to analyze the data and test the hypotheses. The models could explain the measurement indices, and based on the adopted method, the fitting indices of the measurement models showed the acceptability of the measurement models for Islamic education, religious concepts, and medical ethics. Conclusions: In addition to the content-related relationship with medical ethics, the Islamic education promotes ethics in the target community and has a direct impact on the education of medical ethics. Also, the explanation of religious concepts has a major impact on the promotion of the quality of medical ethics, since religious concepts, as students' subjective presuppositions, help them to better understand the content of medical ethics. By the evaluation of the research hypotheses, a direct relationship was observed between the education of Islamic education and promotion of medical ethics. Accordingly, a relationship between the religious concepts and the promotion of medical ethics was also confirmed. The course of Islamic education has a lower impact on medical ethics compared with that of religious concepts. The attention paid by the medical education system to the results of data analysis leads to an increase in the quality of Islamic education course offered to the students. Keywords Islamic Education Religious Concepts Medical Ethics Medical Student

Three Dimensional Root CT Segmentation Using Multi-Resolution Encoder-Decoder Networks

© 1992-2012 IEEE. We address the complex problem of reliably segmenting root structure from soil in X-ray Computed Tomography (CT) images. We utilise a deep learning approach, and propose a state-of-the-art multi-resolution architecture based on encoder-decoders. While previous work in encoder-decoders implies the use of multiple resolutions simply by downsampling and upsampling images, we make this process explicit, with branches of the network tasked separately with obtaining local high-resolution segmentation, and wider low-resolution contextual information. The complete network is a memory efficient implementation that is still able to resolve small root detail in large volumetric images. We compare against a number of different encoder-decoder based architectures from the literature, as well as a popular existing image analysis tool designed for root CT segmentation. We show qualitatively and quantitatively that a multi-resolution approach offers substantial accuracy improvements over a both a small receptive field size in a deep network, or a larger receptive field in a shallower network. We then further improve performance using an incremental learning approach, in which failures in the original network are used to generate harder negative training examples. Our proposed method requires no user interaction, is fully automatic, and identifies large and fine root material throughout the whole volume

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

X‐ray CT reveals 4D root system development and lateral root responses to nitrate in soil

Abstract The spatial arrangement of the root system, termed root system architecture, is important for resource acquisition as it directly affects the soil zone explored. Methods for phenotyping roots are mostly destructive, which prevents analysis of roots over time as they grow. Here, we used X‐ray microcomputed tomography (μCT) to non‐invasively characterize wheat (Triticum aestivum L.) seedling root development across time under high and low nitrate nutrition. Roots were imaged multiple times with the 3D models co‐aligned and timestamped in the architectural plant model OpenSimRoot for subsequent root growth and nitrate uptake simulations. Through 4D imaging, we found that lateral root traits were highly responsive to nitrate limitation in soil with greater lateral root length under low N. The root growth model using all μCT root scans was comparable to a parameterized model using only the final root scan in the series. In a second μCT experiment, root growth and nitrate uptake simulations of candidate wheat genotypes found significant root growth and uptake differences between lines. A high nitrate uptake wheat line selected from field data had a greater lateral root count and length at seedling growth stage compared with a low uptake line