cellshape 0.0.2

A small example package

README

3D single-cell shape analysis of cancer cells using geometric deep learning

This is a package for automatically learning and clustering cell shapes from 3D images. Please refer to our preprint on bioRxiv here

cellshape is available for everyone.

Graph neural network

https://github.com/Sentinal4D/cellshape-cloud Cellshape-cloud is an easy-to-use tool to analyse the shapes of cells using deep learning and, in particular, graph-neural networks. The tool provides the ability to train popular graph-based autoencoders on point cloud data of 2D and 3D single cell masks as well as providing pre-trained networks for inference.

Clustering

https://github.com/Sentinal4D/cellshape-cluster

Cellshape-cluster is an easy-to-use tool to analyse the cluster cells by their shape using deep learning and, in particular, deep-embedded-clustering. The tool provides the ability to train popular graph-based or convolutional autoencoders on point cloud or voxel data of 3D single cell masks as well as providing pre-trained networks for inference.

https://github.com/Sentinal4D/cellshape-voxel

Convolutional neural network

Cellshape-voxel is an easy-to-use tool to analyse the shapes of cells using deep learning and, in particular, 3D convolutional neural networks. The tool provides the ability to train 3D convolutional autoencoders on 3D single cell masks as well as providing pre-trained networks for inference.

Point cloud generation

https://github.com/Sentinal4D/cellshape-helper