This is a short description of BioVIsion's software stack. Feel free to browse our GitHub page to learn more. 


ml-morph is a tool that can be used to collect high-dimensional morphometric data in two-dimensional images of semi-rigid biological structures. It allows for dense and accurate landmarking at low cost, high speed and with minimal impact on specimens.


ALPACA provides fast landmark transfer from a 3D model and its associated landmark set to target 3D model(s) through pointcloud alignment and deformable mesh registration. Optimal set of parameters that gives the best correspondence can be investigated in single alignment mode, and then applied to a number of 3D models in batch mode. ALPACA is a core component of the SlicerMorph project.


DeepBryo is a web application for deep learning-based morphometric characterization of cheilostome bryozoans. DeepBryo requires a single image as input and performs measurements automatically using instance segmentation algorithms. DeepBryo is capable of detecting objects belonging to six classes and outputting fourteen morphological shape measurements for each object based on the inferred segmentation maps. The users can visualize the predictions, check for errors, and directly filter model outputs on the web browser. Measurements can then be downloaded as a comma-separated values file.


Appendometer is a CLI tool for machine-learning-based morphometric characterization of Drosophila leg elements. It performs landmark prediction in Drosophila images using the ml-morph pipeline and saves the output to TPS files.


Steginator is a CLI tool for deep-learning-based morphometric characterization of Steginoporella magnifica zooids. It performs zooid detection using the DeepBryo pipeline and landmark prediction using the ml-morph pipeline and saves the output to TPS files.

PYCPD (Contributor)

PyCPD is a pure numpy implementation of the coherent point drift CPD algorithm by Myronenko and Song for use by the python community. It provides three registration methods for point clouds: 1) Scale and rigid registration; 2) Affine registration; and 3) Gaussian regularized non-rigid registration.