Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation

Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to the relatively small data set for training, data augmentation at training time has been commonly used for better performance of CNNs. Recent works also demonstrated the usefulness of using augmentation at test time, in addition to training time, for achieving more robust predictions. We investigate how test-time augmentation can improve CNNs' performance for brain tumor segmentation. We used different underpinning network structures and augmented the image by 3D rotation, flipping, scaling and adding random noise at both training and test time. Experiments with BraTS 2018 training and validation set show that test-time augmentation helps to improve the brain tumor segmentation accuracy and obtain uncertainty estimation of the segmentation results.Comment: 12 pages, 3 figures, MICCAI BrainLes 201

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.Comment: IEEE Transactions on Medical Imagin

Efficient segmentation and classification of the tumor using improved encoder-decoder architecture in brain MRI images

Primary diagnosis of brain tumors is crucial to improve treatment outcomes for patient survival. T1-weighted contrast-enhanced images of Magnetic Resonance Imaging (MRI) provide the most anatomically relevant images. But even with many advancements, day by day in the medical field, assessing tumor shape, size, segmentation, and classification is very difficult as manual segmentation of MRI images with high precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results. Deep learning algorithms have significantly upgraded the research in the artificial intelligence field and help in better understanding medical images and their further analysis. The work carried out in this paper presents a fully automatic brain tumor segmentation and classification model with encoder-decoder architecture that is an improvisation of traditional UNet architecture achieved by embedding three variants of ResNet like ResNet 50, ResNet 101, and ResNext 50 with proper hyperparameter tuning. Various data augmentation techniques were used to improve the model performance. The overall performance of the model was tested on a publicly available MRI image dataset containing three common types of tumors. The proposed model performed better in comparison to several other deep learning architectures regarding quality parameters including Dice Similarity Coefficient (DSC) and Mean Intersection over Union (Mean IoU) thereby enhancing the tumor analysis

MRI brain tumor segmentation and uncertainty estimation using 3D-UNet architectures

Automation of brain tumor segmentation in 3D magnetic resonance images (MRIs) is key to assess the diagnostic and treatment of the disease. In recent years, convolutional neural networks (CNNs) have shown improved results in the task. However, high memory consumption is still a problem in 3D-CNNs. Moreover, most methods do not include uncertainty information, which is especially critical in medical diagnosis. This work studies 3D encoder-decoder architectures trained with patch-based techniques to reduce memory consumption and decrease the effect of unbalanced data. The different trained models are then used to create an ensemble that leverages the properties of each model, thus increasing the performance. We also introduce voxel-wise uncertainty information, both epistemic and aleatoric using test-time dropout (TTD) and data-augmentation (TTA) respectively. In addition, a hybrid approach is proposed that helps increase the accuracy of the segmentation. The model and uncertainty estimation measurements proposed in this work have been used in the BraTS’20 Challenge for task 1 and 3 regarding tumor segmentation and uncertainty estimation.This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad.Peer ReviewedPostprint (published version