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

Published: 31 March 2020| Version 1 | DOI: 10.17632/jv5r64bv7n.1
Lipeng Xie


This dataset contains three files: 1) the source code of our method, 2) the results of our method by testing on public Dataset (, 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.



Image Segmentation, Medical Image Processing