Pooling-free fully convolutional networks with dense skip connections for semantic segmentation, with application to brain tumor segmentation

Abstract

Segmentation of medical images requires multi-scale information, combining local boundary detection with global context. State-of-the-art convolutional neural network (CNN) architectures for semantic segmentation are often composed of a downsampling path which computes features at multiple scales, followed by an upsampling path, required to recover those features at the same scale as the input image. Skip connections allow features discovered in the downward path to be integrated in the upward path. The downsampling mechanism is typically a pooling operation. However, pooling was introduced in CNNs to enable translation invariance, which is not desirable in segmentation tasks. For this reason, we propose an architecture, based on the recently proposed Densenet, for semantic segmentation, in which pooling has been replaced with dilated convolutions. We also present a variant approach, used in the 2017 BRATS challenge, in which a cascade of densely connected nets is used to first exclude non-brain tissue, and then segment tumor structures. We present results on the validation dataset of the Multimodal Brain Tumor Segmentation Challenge 2017.