μMatch: 3D Shape Correspondence for Biological Image Data

Modern microscopy technologies allow imaging biological objects in 3D over a wide range of spatial and temporal scales, opening the way for a quantitative assessment of morphology. However, establishing a correspondence between objects to be compared, a first necessary step of most shape analysis workflows, remains challenging for soft-tissue objects without striking features allowing them to be landmarked. To address this issue, we introduce the mu Match 3D shape correspondence pipeline. mu Match implements a state-of-the-art correspondence algorithm initially developed for computer graphics and packages it in a streamlined pipeline including tools to carry out all steps from input data pre-processing to classical shape analysis routines. Importantly, mu Match does not require any landmarks on the object surface and establishes correspondence in a fully automated manner. Our open-source method is implemented in Python and can be used to process collections of objects described as triangular meshes. We quantitatively assess the validity of mu Match relying on a well-known benchmark dataset and further demonstrate its reliability by reproducing published results previously obtained through manual landmarking.

Automated summary

Modern microscopy technologies allow for 3D imaging of biological objects, enabling quantitative assessment of morphology. The mu Match pipeline introduces a state-of-the-art shape correspondence algorithm for soft-tissue objects without the need for landmarks, establishing correspondence in a fully automated manner.