CANet: Context Aware Network for Brain Glioma Segmentation (original) (raw)
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CANet: Context Aware Network for 3D Brain Glioma Segmentation
2021
Automated segmentation of brain glioma plays an active role in diagnosis decision, progression monitoring and surgery planning. Based on deep neural networks, previous studies have shown promising technologies for brain glioma segmentation. However, these approaches lack powerful strategies to incorporate contextual information of tumor cells and their surrounding, which has been proven as a fundamental cue to deal with local ambiguity. In this work, we propose a novel approach named Context-Aware Network (CANet) for brain glioma segmentation. CANet captures high dimensional and discriminative features with contexts from both the convolutional space and feature interaction graphs. We further propose context guided attentive conditional random fields which can selectively aggregate features. We evaluate our method using publicly accessible brain glioma segmentation datasets BRATS2017, BRATS2018 and BRATS2019. The experimental results show that the proposed algorithm has better or com...
CANet: Context Aware Network for 3D Brain Tumor Segmentation
ArXiv, 2020
Automated segmentation of brain tumors in 3D magnetic resonance imaging plays an active role in tumor diagnosis, progression monitoring and surgery planning. Based on convolutional neural networks, especially fully convolutional networks, previous studies have shown some promising technologies for brain tumor segmentation. However, these approaches lack suitable strategies to incorporate contextual information to deal with local ambiguities, leading to unsatisfactory segmentation outcomes in challenging circumstances. In this work, we propose a novel Context-Aware Network (CANet) with a Hybrid Context Aware Feature Extractor (HCA-FE) and a Context Guided Attentive Conditional Random Field (CG-ACRF) for feature fusion. HCA-FE captures high dimensional and discriminative features with the contexts from both the convolutional space and feature interaction graphs. We adopt the powerful inference ability of probabilistic graphical models to learn hidden feature maps, and then use CG-ACRF...
Global Planar Convolutions for Improved Context Aggregation in Brain Tumor Segmentation
Lecture Notes in Computer Science, 2019
In this work, we introduce the Global Planar Convolution module as a building-block for fully-convolutional networks that aggregates global information and, therefore, enhances the context perception capabilities of segmentation networks in the context of brain tumor segmentation. We implement two baseline architectures (3D UNet and a residual version of 3D UNet, ResUNet) and present a novel architecture based on these two architectures, ContextNet, that includes the proposed Global Planar Convolution module. We show that the addition of such module eliminates the need of building networks with several representation levels, which tend to be over-parametrized and to showcase slow rates of convergence. Furthermore, we provide a visual demonstration of the behavior of GPC modules via visualization of intermediate representations. We finally participate in the 2018 edition of the BraTS challenge with our best performing models, that are based on ContextNet, and report the evaluation scores on the validation and the test sets of the challenge.
Brain Tumour Segmentation Using S-Net and SA-Net
IEEE Access, 2023
Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves extracting a set of contours from the input digital image so that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread and includes video surveillance, object detection, traffic control system, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, a study of anatomical structures, measurement of tissue volumes, location of tumours, and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) SA-Net (S-Net with attention mechanism) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI) scans. Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of 'Merge Block' to infuse both the local and global context and 'Attention Block' to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measures of 0.78 and 0.81 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively. INDEX TERMS Attention block, brain tumour segmentation, convolutional neural network, deep learning, high-grade glioma, low-grade glioma, merge block, U-Net. • We designed a model by considering a lower number of convolutional layers to define both the down-convolution and up-convolution operations, hence reducing the computational complexity without compromising on the performance measures.
TuNet: End-to-End Hierarchical Brain Tumor Segmentation Using Cascaded Networks
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2020
Glioma is one of the most common types of brain tumors; it arises in the glial cells in the human brain and in the spinal cord. In addition to having a high mortality rate, glioma treatment is also very expensive. Hence, automatic and accurate segmentation and measurement from the early stages are critical in order to prolong the survival rates of the patients and to reduce the costs of the treatment. In the present work, we propose a novel end-to-end cascaded network for semantic segmentation that utilizes the hierarchical structure of the tumor sub-regions with ResNet-like blocks and Squeeze-and-Excitation modules after each convolution and concatenation block. By utilizing cross-validation, an average ensemble technique, and a simple post-processing technique, we obtained dice scores of 88.06, 80.84, and 80.29, and Hausdorff Distances (95th percentile) of 6.10, 5.17, and 2.21 for the whole tumor, tumor core, and enhancing tumor, respectively, on the online test set.
Glioma Segmentation with Cascaded UNet
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries, 2019
MRI analysis takes central position in brain tumor diagnosis and treatment, thus it's precise evaluation is crucially important. However, it's 3D nature imposes several challenges, so the analysis is often performed on 2D projections that reduces the complexity, but increases bias. On the other hand, time consuming 3D evaluation, like, segmentation, is able to provide precise estimation of a number of valuable spatial characteristics, giving us understanding about the course of the disease. Recent studies, focusing on the segmentation task, report superior performance of Deep Learning methods compared to classical computer vision algorithms. But still, it remains a challenging problem. In this paper we present deep cascaded approach for automatic brain tumor segmentation. Similar to recent methods for object detection, our implementation is based on neural networks; we propose modifications to the 3D UNet architecture and augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data. We evaluate presented approach on BraTS 2018 dataset and discuss results.
A Novel Approach for Fully Automatic Intra-Tumor Segmentation With 3D U-Net Architecture for Gliomas
Frontiers in Computational Neuroscience, 2020
Purpose: Gliomas are the most common primary brain malignancies, with varying degrees of aggressiveness and prognosis. Understanding of tumor biology and intra-tumor heterogeneity is necessary for planning personalized therapy and predicting response to therapy. Accurate tumoral and intra-tumoral segmentation on MRI is the first step toward understanding the tumor biology through computational methods. The purpose of this study was to design a segmentation algorithm and evaluate its performance on pre-treatment brain MRIs obtained from patients with gliomas. Materials and Methods: In this study, we have designed a novel 3D U-Net architecture that segments various radiologically identifiable sub-regions like edema, enhancing tumor, and necrosis. Weighted patch extraction scheme from the tumor border regions is proposed to address the problem of class imbalance between tumor and non-tumorous patches. The architecture consists of a contracting path to capture context and the symmetric expanding path that enables precise localization. The Deep Convolutional Neural Network (DCNN) based architecture is trained on 285 patients, validated on 66 patients and tested on 191 patients with Glioma from Brain Tumor Segmentation (BraTS) 2018 challenge dataset. Three dimensional patches are extracted from multi-channel BraTS training dataset to train 3D U-Net architecture. The efficacy of the proposed approach is also tested on an independent dataset of 40 patients with High Grade Glioma from our tertiary cancer center. Segmentation results are assessed in terms of Dice Score, Sensitivity, Specificity, and Hausdorff 95 distance (ITCN intra-tumoral classification network). Result: Our proposed architecture achieved Dice scores of 0.88, 0.83, and 0.75 for the whole tumor, tumor core and enhancing tumor, respectively, on BraTS validation dataset and 0.85, 0.77, 0.67 on test dataset. The results were similar on the independent patients' dataset from our hospital, achieving Dice scores of 0.92, 0.90, and 0.81 for the whole tumor, tumor core and enhancing tumor, respectively. Baid et al. Automatic Intra-Tumor Segmentation for Gliomas Conclusion: The results of this study show the potential of patch-based 3D U-Net for the accurate intra-tumor segmentation. From experiments, it is observed that the weighted patch-based segmentation approach gives comparable performance with the pixel-based approach when there is a thin boundary between tumor subparts.
Hierarchical segmentation of malignant gliomas via integrated contextual filter response
Medical Imaging 2008: Image Processing, 2008
We present a novel methodology for the automated segmentation of Glioblastoma Multiforme tumors given only a highresolution T1 post-contrast enhanced channel, which is routinely done in clinical MR acquisitions. The main contribution of the paper is the integration of contextual filter responses, to obtain a better class separation of abnormal and normal brain tissues, into the multilevel segmentation by weighted aggregation (SWA) algorithm. The SWA algorithm uses neighboring voxel intensities to form an affinity between the respective voxels. The affinities are then recursively computed for all the voxel pairs in the given image and a series of cuts are made to produce segments that contain voxels with similar intensity properties. SWA provides a fast method of partitioning the image, but does not produce segments with meaning. Thus, a contextual filter response component was integrated to label the aggregates as tumor or non-tumor. The contextual filter responses were computed via texture filter responses based on the gray level co-occurrence matrix (GLCM) method. The GLCM results in texture features that are used to quantify the visual appearance of the tumor versus normal tissue. Our results indicate the benefit of incorporating contextual features and applying non-linear classification methods to segment and classify the complex case of grade 4 tumors.
2019 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS), 2019
A Brain tumor is an abnormal cell growth in the brain tissues, these tumors are difficult to treat and severely affect the patient's cognitive ability. Out of all brain tumors, gliomas are the deadliest with the least survival rate. The focus of brain tumor segmentation task is to separate tumor tissue such as edema, tumor core from the healthy tissues i.e. white cells, Cerebrospinal Fluid and gray matter. Manual diagnosis of brain tumors from a large amount of patient's MRI images is a tough and time-taking process. With the advent of new approaches, automatic segmentation processes are becoming more effective and clinically accepted. This paper aims to give a comprehensive review of the most state of the art brain tumor segmentation methods. We have given a brief introduction to the imaging modalities and their usage in brain tumor segmentation task. We have discussed the results of the most effective approaches by comparing their Dice Score results. We have also discussed some publicly available brain datasets. Furthermore, we have presented a Novel approach for Glioma tumor segmentation using ResNeXt architecture. Experimental results prove that our framework performs well on the dice score.
Studies in Health Technology and Informatics
In this article, we compare the performance of a state-of-the-art segmentation network (UNet) on two different glioblastoma (GB) segmentation datasets. Our experiments show that the same training procedure yields almost twice as bad results on the retrospective clinical data compared to the BraTS challenge data (in terms of Dice score). We discuss possible reasons for such an outcome, including inter-rater variability and high variability in magnetic resonance imaging (MRI) scanners and scanner settings. The high performance of segmentation models, demonstrated on preselected imaging data, does not bring the community closer to using these algorithms in clinical settings. We believe that a clinically applicable deep learning architecture requires a shift from unified datasets to heterogeneous data.