Nuclei Segmentation
This contains all my implementations for DSB2018. My first pass through data contained an implemented of FCN, and I later went on to implement two versions of U-Net. Initial data exploration where I found some outliers in data along with FCN (Fully Convolutional Network) are available in the notebook.
First Approach:
I used a very large U-Net for semantic segmentation of nuclei cells. See results below:
Second Approach
In the second attempt, I used k-means clustering on images to produce additional images with different number of k (see below). This proved to be a good strategy as each images contained little variations in colors. All these images were combined to form a 7-channel image and fed to the U-Net. See results below.
How to Run
To start training, simply run main.py file. All parameters are made available at the top under constants. Module descriptions are provided below:
dataloader.py-- loads the data, provides batches for training with augmentation enabled, generates k-means cluster maps.model.py-- provides functions that return unet models for approach 1 and 2.trainer.py-- when passed a dataloader object and model, it will train it with proper logging for training and validation to tensorboard (shown below).main.py-- main file to run.
Tensorboard
