Deep Active Self-paced Learning for Accurate Pulmonary Nodule Segmentation

Abstract

Automatic and accurate pulmonary nodule segmentation in lung Computed Tomography (CT) volumes plays an important role in computer-aided diagnosis of lung cancer. However, this task is challenging due to target/background voxel imbalance and the lack of voxel-level annotation. In this paper, we propose a novel deep region-based network, called Nodule R-CNN, for efficiently detecting pulmonary nodules in 3D CT images while simultaneously generating a segmentation mask for each instance. Also, we propose a novel Deep Active Self-paced Learning (DASL) strategy to reduce annotation effort and also make use of unannotated samples, based on a combination of Active Learning and Self-Paced Learning (SPL) schemes. Experimental results on the public LIDC-IDRI dataset show our Nodule R-CNN achieves state-of-the-art results on pulmonary nodule segmentation, and Nodule R-CNN trained with the DASL strategy performs much better than Nodule R-CNN trained without DASL using the same amount of annotated samples.