Department of Computer Science and Technology, University of South Carolina, Columbia, SC, 29208, USA.
Department of Medical Information Engineering, Zunyi Medical University, Zunyi, China.
Med Biol Eng Comput. 2019 Sep;57(9):2027-2043. doi: 10.1007/s11517-019-02008-8. Epub 2019 Jul 26.
This paper addresses the task of nuclei segmentation in high-resolution histopathology 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-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation . Graphical Abstract The neural network for nuclei segmentation.
本文针对高分辨率组织病理学图像中的细胞核分割任务。我们提出了一种自动端到端的深度学习神经网络算法，用于分割单个细胞核。引入了一个细胞核边界模型，该模型使用全卷积神经网络同时预测细胞核及其边界。给定一个颜色归一化的图像，模型直接输出一个估计的细胞核图和一个边界图。在估计的细胞核图上执行一个简单、快速且无参数的后处理过程，以生成最终分割的细胞核。还设计了一种重叠补丁提取和组装方法，用于无缝预测大整张幻灯片图像中的细胞核。我们还展示了数据增强方法对细胞核分割任务的有效性。我们的实验表明，我们的方法优于先前的最先进方法。此外，它的效率很高，一个 1000×1000 的图像可以在不到 5 秒的时间内分割。这使得在可接受的时间内精确分割整张幻灯片图像成为可能。源代码可在 https://github.com/easycui/nuclei_segmentation 获得。
