Abstract
DeCA (Dense Correspondence Analysis) is an open-source toolkit for biologists that integrates biological insights in the form of homologous landmark points with dense surface registration to provide highly detailed shape analysis of smooth and complex structures that are typically challenging to analyze with sparse manual landmarks alone. In this work we demonstrate the use of DeCA by analyzing morphological differences of the skull in a dataset of 60 laboratory mice from different background strains.
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Acknowledgement
Parts of this research were funded by the National Science Foundation Award [OAC 2118240] (Imageomics Institute) and National Institute of Dental and Craniofacial Research (DE027110) to AMM.
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Rolfe, S.M., Maga, A.M. (2023). DeCA: A Dense Correspondence Analysis Toolkit for Shape Analysis. In: Wachinger, C., Paniagua, B., Elhabian, S., Li, J., Egger, J. (eds) Shape in Medical Imaging. ShapeMI 2023. Lecture Notes in Computer Science, vol 14350. Springer, Cham. https://doi.org/10.1007/978-3-031-46914-5_21
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DOI: https://doi.org/10.1007/978-3-031-46914-5_21
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