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.我们提出了一种用于重叠细胞核分割的新颖高效计算框架，该框架将标记控制分水岭算法（Marker-controlled Watershed）与我们提出的卷积神经网络（DIMAN）相结合。本方法基于开源机器学习框架TensorFlow与强化学习库TensorLayer实现。本代码仓库包含了我们实验中使用的全部代码，涵盖数据准备、模型构建、模型训练与结果评估全流程。为与本方法进行对比，我们同样基于TensorFlow与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与SharpMask。此外，我们还将本方法与四项已发表的当前最优（state-of-the-art）方法进行了对比。