Data for: Integrating deep convolutional neural networks with marker-controlled watershed for overlapping nuclei segmentation in histopathology images

This dataset contains three files: 1) the source code of our method, 2) the results of our method by testing on public Dataset (https://nucleisegmentationbenchmark.weebly.com/), 3) the gif picture, which shows the effect of our method applied in the whole slide image. In our paper, we present a novel and efficient computing framework for segmenting the overlapping nuclei by combining Marker-controlled Watershed with our proposed convolutional neural network (DIMAN). We implemented our method based on the open-source machine learning framework TensorFlow and reinforcement learning library TensorLayer.This repository contains all code used in our experiments, incuding the data preparation, model construction, model training and result evaluation. For comparison with our method, we also utilized TensorFlow and TensorLayer to reimplement four known semantic segmentation convolutional neural networks: FCN8s, U-Net, HED and SharpMask.

本数据集包含三个文件：1）我们所提方法的源代码；2）我们的方法在公开数据集（https://nucleisegmentationbenchmark.weebly.com/）上测试所得的结果；3）用于展示我们的方法应用于全视野切片图像效果的GIF动图。在本论文中，我们提出了一种新颖高效的计算框架，通过将标记控制分水岭算法与我们提出的卷积神经网络（convolutional neural network，DIMAN）相结合，实现重叠细胞核的分割任务。我们基于开源机器学习框架TensorFlow以及强化学习库TensorLayer实现了所提方法。本代码仓库包含了我们实验中用到的全部代码，涵盖数据预处理、模型构建、模型训练以及结果评估环节。为了与我们的方法进行对比实验，我们同样基于TensorFlow和TensorLayer重新实现了四种经典的语义分割卷积神经网络：FCN8s、U-Net、HED以及SharpMask。