pyssam -- a Python library for statistical modelling of biomedical shape and appearance

TL;DR

pyssam is a Python library that enables creation of statistical shape and appearance models from biological shape data, facilitating analysis of shape variation and integration of appearance features.

Contribution

The paper introduces pyssam, a lightweight Python library for constructing SSAMs from landmark data, including shape and appearance, with tools for alignment, scaling, and modeling.

Findings

Provides a flexible tool for biological shape analysis.

Includes examples for medical image segmentation.

Supports joint modeling of shape and appearance features.

Abstract

pyssam is a Python library for creating statistical shape and appearance models (SSAMs) for biological (and other) shapes such as bones, lungs or other organs. A point cloud best describing the anatomical 'landmarks' of the organ are required from each sample in a small population as an input. Additional information such as landmark gray-value can be included to incorporate joint correlations of shape and 'appearance' into the model. Our library performs alignment and scaling of the input data and creates a SSAM based on covariance across the population. The output SSAM can be used to parameterise and quantify shape change across a population. pyssam is a small and low dependency codebase with examples included as Jupyter notebooks for several common SSAM computations. The given examples can easily be extended to alternative datasets, and also alternative tasks such as medical image segmentation by incorporating a SSAM as a constraint for segmented organs.