PiPo-Net: A Semi-automatic and Polygon-based Annotation Method for Pathological Images

Abstract

Metastatic involvement of lymph nodes is one of the most important prognostic variables for many cancers. Several deep learning based algorithms have been developed to segment metastatic regions in pathological images to help predict prognosis. However, the training of these methods requires a large amount of annotated data, and the labeling task is an extremely time-consuming process for human annotators. In order to reduce the annotation burden, we for the first time propose a semi-automatic annotation method (PiPo-Net) for the labeling of pathological images. The method is comprised of two subnetworks, a pixel-wise segmentation network (Pi-Net) and a polygon-based annotation network (Po-Net). The Pi-Net adopts an improved encoder-decoder architecture and can effectively aggregate multi-scale image features. The Po-Net is built on the Pi-Net and leverages a two-layer recurrent neural network to generate tight-bounded polygons for the metastatic regions. Corresponding to the proposed network architecture, a loss function called PiPo-loss is introduced to help optimize the whole network. The main advantage of our method is that it integrates human annotators into the prediction loop, allowing to iteratively refine the predictions according to the suggestions from human annotators. We evaluate our method on Camelyon16 database and achieve a Dice score of 91% in the initial annotation attempt. We also demonstrate the effectiveness of the human-network collaborative annotation, which achieves promising labeling results, verifying the advantages of our proposed method.