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

TBI lesion segmentation in head CT: impact of preprocessing and data augmentation

Automatic segmentation of lesions in head CT provides keyinformation for patient management, prognosis and disease monitoring.Despite its clinical importance, method development has mostly focusedon multi-parametric MRI. Analysis of the brain in CT is challengingdue to limited soft tissue contrast and its mono-modal nature. We studythe under-explored problem of fine-grained CT segmentation of multiplelesion types (core, blood, oedema) in traumatic brain injury (TBI). Weobserve that preprocessing and data augmentation choices greatly impactthe segmentation accuracy of a neural network, yet these factors arerarely thoroughly assessed in prior work. We design an empirical studythat extensively evaluates the impact of different data preprocessing andaugmentation methods. We show that these choices can have an impactof up to 18% DSC. We conclude that resampling to isotropic resolutionyields improved performance, skull-stripping can be replaced by using theright intensity window, and affine-to-atlas registration is not necessaryif we use sufficient spatial augmentation. Since both skull-stripping andaffine-to-atlas registration are susceptible to failure, we recommend theiralternatives to be used in practice. We believe this is the first work toreport results for fine-grained multi-class segmentation of TBI in CT. Ourfindings may inform further research in this under-explored yet clinicallyimportant task of automatic head CT lesion segmentation

Ensembles of Multiple Models and Architectures for Robust Brain Tumour Segmentation

Deep learning approaches such as convolutional neural nets have consistently outperformed previous methods on challenging tasks such as dense, semantic segmentation. However, the various proposed networks perform differently, with behaviour largely influenced by architectural choices and training settings. This paper explores Ensembles of Multiple Models and Architectures (EMMA) for robust performance through aggregation of predictions from a wide range of methods. The approach reduces the influence of the meta-parameters of individual models and the risk of overfitting the configuration to a particular database. EMMA can be seen as an unbiased, generic deep learning model which is shown to yield excellent performance, winning the first position in the BRATS 2017 competition among 50+ participating teams.Comment: The method won the 1st-place in the Brain Tumour Segmentation (BRATS) 2017 competition (segmentation task