Document - Automatic image-based coral polyp analysis through multi-view instance segmentation

2025

Contribution to conference Open Access

Automatic image-based coral polyp analysis through multi-view instance segmentation

Dutta S., Pavoni G., Cattini S., Rovati L., Capra A., Castagnetti C., Corsini M., Ganovelli F., Cignoni P., Rossi P., Cenni E., Simonini R., Grassi F., Cassanelli D.

Object detection, 3D segmentation, Shape analysis

We present an automated framework for counting and measuring the polyps of Cladocora caespitosa, a Mediterranean reefbuilding coral. To our knowledge, the most practical method for counting polyps currently involves ecologists’ visual inspection of a 3D model. However, measuring polyps from the model can lead to inaccuracies due to distortions in the reconstruction. Our method integrates deep learning-based instance segmentation on 2D images with 3D models for unique polyp identification, ensuring precise biometric extraction. The proposed pipeline automates polyp detection, counting, and measurement while overcoming the limitations of manual in situ methods. Laboratory validation demonstrates its accuracy and efficiency, paving the way for scalable, high-resolution phenotyping, and field monitoring of Mediterranean coral populations.

Publisher: Eurographics

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@inproceedings{oai:iris.cnr.it:20.500.14243/566983,
	title = {Automatic image-based coral polyp analysis through multi-view instance segmentation},
	author = {Dutta S. and Pavoni G. and Cattini S. and Rovati L. and Capra A. and Castagnetti C. and Corsini M. and Ganovelli F. and Cignoni P. and Rossi P. and Cenni E. and Simonini R. and Grassi F. and Cassanelli D.},
	publisher = {Eurographics},
	doi = {10.2312/egp.20251022},
	year = {2025}
}