Abstract-This paper addresses the task of nuclei segmentation in high-resolution histopathological images. We propose an automatic end-to-end deep neural network algorithm for segmentation 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-theart 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.
In 2018, another very promising deep learning-based one-step contour aware nuclei segmentation approach is proposed where a fully convolutional neural network is applied to segment the nuclei with their boundaries simultaneously REF .
A Deep Learning Algorithm for One-step Contour Aware Nuclei Segmentation of Histopathological Images
Med Biol Eng Comput 2019
Mathematics Medicine Computer Science