3D-BoxSup: Positive-Unlabeled Learning of Brain Tumor Segmentation Networks From 3D Bounding Boxes.

ABSTRACT: Accurate segmentation is an essential task when working with medical images. Recently, deep convolutional neural networks achieved a state-of-the-art performance for many segmentation benchmarks. Regardless of the network architecture, the deep learning-based segmentation methods view the segmentation problem as a supervised task that requires a relatively large number of annotated images. Acquiring a large number of annotated medical images is time consuming, and high-quality segmented images (i.e., strong labels) crafted by human experts are expensive. In this paper, we have proposed a method that achieves competitive accuracy from a "weakly annotated" image where the weak annotation is obtained via a 3D bounding box denoting an object of interest. Our method, called "3D-BoxSup," employs

SUBMITTER: Xu Y 

PROVIDER: S-EPMC7199456 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature