MS-Net: Mixed-Supervision Fully-Convolutional Networks for Full-Resolution Segmentation

For image segmentation, typical fully convolutional networks (FCNs) need strong supervision through a large sample of high-quality dense segmentations, entailing high costs in expert-raters’ time and effort. We propose MS-Net, a new FCN to significantly reduce supervision cost, and improve performance, by coupling strong supervision with weak supervision through low-cost input in the form of bounding boxes and landmarks. Our MS-Net enables instance-level segmentation at high spatial resolution, with feature extraction using dilated convolutions. We propose a new loss function using bootstrapped Dice overlap for precise segmentation. Results on large datasets show that MS-Net segments more accurately at reduced supervision costs, compared to the state of the art.