3D-BoxSup: Positive-Unlabeled Learning of Brain Tumor Segmentation Networks From 3D Bounding Boxes - Xu, Yanwu; Gong, Mingming; Chen, Junxiang; Chen, Ziye; Batmanghelich, Kayhan (Frontiers in Neuroscience, 2020)
Accurate segmentation is one of the essential tasks when working with medical images. Recently, deep convolutional neural networks achieved state-of-the-art performance in 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) by human experts are expensive to collect. In this paper, we propose 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 a positive-unlabeled learning framework to learn segmentation masks from 3D bounding boxes. Specially, we consider the pixels outside of the bounding box as positively-labeled data and the pixels inside the bounding box as unlabeled data. Our method can suppress the negative effects of pixels residing between the true segmentation mask and the 3D bounding box and produce accurate segmentation masks. We apply our method to segment brain tumor. The experimental results on the BraTS 2017 dataset demonstrate the effectiveness of our method.