Nuclei_Segmentation_Experiments_Demo_By_DIMAN

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. Beside this, we also compare our method with four published state-of-art methods.