gpssi 0.1.2

Sampling image segmentations with Gaussian process

gpssi

This repository provides code for the paper:

Lê, Matthieu, et al. "Sampling image segmentations for uncertainty quantification." Medical image analysis 34 (2016): 42-51. doi:10.1016/j.media.2016.04.005

Note that this is not an official implementation by the authors and was motivated by the lack of publicly available code. Be aware that there might be difference to the original implementation.

Implementation Details

Geodesic map

To produce the geodesic maps, this project relies on the GeodisTK packages. This package can be installed via pip or via source code (https://github.com/taigw/GeodisTK). Due to observed issues when installing the package via pip, we suggest to install it from the github link (see installation).

Factorization via Kronecker

The authors use a Kronecker matrix representation of the covariance matrix to overcome the issue of large covariance matrices.

This project implements the kronecker matrix-vector product based on following reference:

• Saatçi, Yunus. Scalable inference for structured Gaussian process models. Diss. University of Cambridge, 2012.
• Gilboa, Elad, Yunus Saatçi, and John P. Cunningham. "Scaling multidimensional inference for structured Gaussian processes." IEEE transactions on pattern analysis and machine intelligence 37.2 (2013): 424-436.

Installation

This projects is available as python package and can be installed by

pip install gpssi

Otherwise, the package can also from the source code via

git clone https://github.com/alainjungo/gpssi.git
cd gpssi
pip install .

The GeodisTK package is not installed automatically and has to be installed manually. We propose to use the direct installation via source code:

pip install git+https://github.com/taigw/GeodisTK.git

Alternatively, you can try installing it from pypi (pip install GeodisTK)

Usage

See gpssi_example.py for an example usage.