Automated brain tumour detection and segmentation using superpixel-based extremely randomized trees in FLAIR MRI

PURPOSE: We propose a fully automated method for detection and segmentation of the abnormal tissue associated with brain tumour (tumour core and oedema) from Fluid- Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Imaging (MRI). METHODS: The method is based on superpixel technique and classification of each superpixel. A number of novel 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 randomized trees (ERT) classifier is compared with support vector machine (SVM) to classify each superpixel into tumour and non-tumour. RESULTS: The proposed method is evaluated on two datasets: (1) Our own clinical dataset: 19 MRI FLAIR images of patients with gliomas of grade II to IV, and (2) BRATS 2012 dataset: 30 FLAIR images with 10 low-grade and 20 high-grade gliomas. The experimental results demonstrate the high detection and segmentation performance of the proposed method using ERT classifier. For our own cohort, the average detection sensitivity, balanced error rate and the Dice overlap measure for the segmented tumour against the ground truth are 89.48 %, 6 % and 0.91, respectively, while, for the BRATS dataset, the corresponding evaluation results are 88.09 %, 6 % and 0.88, respectively. CONCLUSIONS: 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

Nonparametric density flows for MRI intensity normalisation

With the adoption of powerful machine learning methods in medical image analysis, it is becoming increasingly desirable to aggregate data that is acquired across multiple sites. However, the underlying assumption of many analysis techniques that corresponding tissues have consistent intensities in all images is often violated in multi-centre databases. We introduce a novel intensity normalisation scheme based on density matching, wherein the histograms are modelled as Dirichlet process Gaussian mixtures. The source mixture model is transformed to minimise its L2 divergence towards a target model, then the voxel intensities are transported through a mass-conserving flow to maintain agreement with the moving density. In a multi-centre study with brain MRI data, we show that the proposed technique produces excellent correspondence between the matched densities and histograms. We further demonstrate that our method makes tissue intensity statistics substantially more compatible between images than a baseline affine transformation and is comparable to state-of-the-art while providing considerably smoother transformations. Finally, we validate that nonlinear intensity normalisation is a step toward effective imaging data harmonisation

Learning Sample-Adaptive Intensity Lookup Table for Brain Tumor Segmentation

© 2020, Springer Nature Switzerland AG. Intensity variation among MR images increases the difficulty of training a segmentation model and generalizing it to unseen MR images. To solve this problem, we propose to learn a sample-adaptive intensity lookup table (LuT) that adjusts each image’s contrast dynamically so that the resulting images could better serve the subsequent segmentation task. Specifically, our proposed deep SA-LuT-Net consists of an LuT module and a segmentation module, trained in an end-to-end manner: the LuT module learns a sample-specific piece-wise linear intensity mapping function under the guide of the performance of the segmentation module. We develop our SA-LuT-Nets based on two backbone networks, DMFNet and the modified 3D Unet, respectively, and validate them on BRATS2018 dataset for brain tumor segmentation. Our experiment results clearly show the effectiveness of SA-LuT-Net in the scenarios of both single and multi-modalities, which is superior over the two baselines and many other relevant state-of-the-art segmentation models

On the compactness, efficiency, and representation of 3D convolutional networks:Brain parcellation as a pretext task

Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.Comment: Paper accepted at IPMI 201

Techniques of virtual dissection of the colon based on spiral CT data

Colorectal cancer represents the third most commonly diagnosed cancer and is the second leading cause of cancer deaths in the United States (Gazelle et al. 2000). In addition, colorectal cancer is responsible for about 11% of all new cancer cases per year (Gazelle et al. 2000). Five-year prognosis is about 90% for patients with localized disease compared to 60% if there is a regional spread and a drop to 10% in patients with distant metastasis (Gazelle et al. 2000). In the field of medicine there is a widely accepted opinion that most colorectal cancers arise from pre-existent adenomatous polyps (Johnson 2000). Therefore, different societies, such as the American Cancer Society, have proposed screening for colorectal cancer (Byers et al. 1997; Winawer et al. 1997). Today, different options exist for detection of colorectal cancer, including digital rectal examination, fecal occult blood testing, flexible and rigid sigmoidoscopy, barium enema and its variants, colonoscopy and recently computed tomography or magnetic resonance-based virtual colonography (Gazelle et al. 2000)

Context Aware 3D CNNs for Brain Tumor Segmentation

International audienceIn this work we propose a novel deep learning based pipeline for the task of brain tumor segmentation. Our pipeline consists of three primary components: (i) a preprocessing stage that exploits histogram standardization to mitigate inaccuracies in measured brain modalities, (ii) a first prediction stage that uses the V-Net deep learning architecture to output dense, per voxel class probabilities, and (iii) a prediction refinement stage that uses a Conditional Random Field (CRF) with a bilateral filtering objective for better context awareness. Additionally, we compare the V-Net architecture with a custom 3D Residual Network architecture, trained on a multi-view strategy, and our ablation experiments indicate that V-Net outperforms the 3D ResNet-18 with all bells and whistles, while fully connected CRFs as post processing, boost the performance of both networks. We report competitive results on the BraTS 2018 validation and test set