Weakly Supervised Learning of Recurrent Residual ConvNets for Pancreas Segmentation in CT Scans Deep neural networks trained by medical images with dense annotations have revealed favourable performance on accurate organ segmentation. The current supervised methods demand voxellevel annotations which are not easily accessible due to the consuming of time and requirements of specialized knowledge and skills. In this paper, we propose a weakly supervised method based on a recurrent residual convolutional neural network trained only with imagelevel labels to generate voxellevel segmentation. The recurrent residual convolutional units take advantage of contextual information of successive slices and a spatial pooling layer is introduced after the last convolutional layer to aggregate local features and learn accurate localization. The final segmentation mask is computed by applying a conditional random field for spatial prediction. Our method shows competitive performance to fully supervised methods on the public NIHCT82 dataset for pancreas segmentation.

EndtoEnd Boundary Aware Networks for Medical Image Segmentation. Fully convolutional neural networks (CNNs) have proven to be effective at representing and classifying textural information, thus transforming image intensity into output class masks that achieve semantic image segmentation. In medical image analysis, however, expert manual segmentation often relies on the boundaries of anatomical structures of interest. We propose boundary aware CNNs for medical image segmentation. Our networks are designed to account for organ boundary information, both by providing a special network edge branch and edgeaware loss terms, and they are trainable endtoend. We validate their effectiveness on the task of brain tumor segmentation using the BraTS 2018 dataset. Our experiments reveal that our approach yields more accurate segmentation results, which makes it promising for more extensive application to medical image segmentation.