A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images

TL;DR

This paper introduces a deep learning-based, end-to-end nuclei segmentation algorithm that predicts nuclei and boundaries simultaneously, enabling fast and accurate segmentation of large histopathological images with minimal post-processing.

Contribution

The paper presents a novel fully convolutional neural network with a nucleus-boundary model and an efficient patch-based approach for large image segmentation, outperforming prior methods.

Findings

Outperforms prior state-of-the-art methods.

Segments a 1000x1000 image in less than 5 seconds.

Effective data augmentation improves segmentation accuracy.

Abstract

This paper addresses the task of nuclei segmentation in high-resolution histopathological images. We propose an auto- matic end-to-end deep neural network algorithm for segmenta- tion of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the- art methods. Moreover, it is efficient that one 1000X1000 image can be segmented in less than 5 seconds. This makes it possible to precisely segment the whole-slide image in acceptable time