DeepAtlas: Joint Semi-Supervised Learning of Image Registration and Segmentation.

ABSTRACT: Deep convolutional neural networks (CNNs) are state-of-the-art for semantic image segmentation, but typically require many labeled training samples. Obtaining 3D segmentations of medical images for supervised training is difficult and labor intensive. Motivated by classical approaches for joint segmentation and registration we therefore propose a deep learning framework that jointly learns networks for image registration and image segmentation. In contrast to previous work on deep unsupervised image registration, which showed the benefit of weak supervision via image segmentations, our approach can use existing segmentations when available and computes them via the segmentation network otherwise, thereby providing the same registration benefit. Conversely, segmentation network training ben

SUBMITTER: Xu Z 

PROVIDER: S-EPMC11378322 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature