Deep Reinforcement Learning for Weakly-Supervised Lymph Node Segmentation in CT Images.

Abstract

Accurate and automated lymph node segmentation is pivotal for quantitatively accessing disease progression and potential therapeutics. The complex variation of lymph node morphology and the difficulty of acquiring voxel-wise manual annotations make lymph node segmentation a challenging task. Since the Response Evaluation Criteria in Solid Tumors (RECIST) annotation, which indicates the location, length, and width of a lymph node, is commonly available in hospital data archives, we advocate to use RECIST annotations as the supervision, and thus formulate this segmentation task into a weakly-supervised learning problem. In this paper, we propose a deep reinforcement learning-based lymph node segmentation (DRL-LNS) model. Based on RECIST annotations, we segment RECIST-slices in an unsupervised way to produce pseudo ground truths, which are then used to train U-Net as a segmentation network. Next, we train a DRL model, in which the segmentation network interacts with the policy network to optimize the lymph node bounding boxes and segmentation results simultaneously. The proposed DRL-LNS model was evaluated against three widely used image segmentation networks on a public thoracoabdominal Computed Tomography (CT) dataset that contains 984 3D lymph nodes, and achieves the mean Dice similarity coefficient (DSC) of 77.17% and the mean Intersection over Union (IoU) of 64.78% in the four-fold cross-validation. Our results suggest that the DRL-based bounding box prediction strategy outperforms the label propagation strategy and the proposed DRL-LNS model is able to achieve the state-of-the-art performance on this weakly-supervised lymph node segmentation task.