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• W4383301751 abstract "Shape measurements are crucial for evolutionary and developmental biology; however, they present difficulties in the objective and automatic quantification of arbitrary shapes. Conventional approaches are based on anatomically prominent landmarks, which require manual annotations by experts. Here, we develop a machine-learning approach by presenting morphological regulated variational AutoEncoder (Morpho-VAE), an image-based deep learning framework, to conduct landmark-free shape analysis. The proposed architecture combines the unsupervised and supervised learning models to reduce dimensionality by focusing on morphological features that distinguish data with different labels. We applied the method to primate mandible image data. The extracted morphological features reflected the characteristics of the families to which the organisms belonged, despite the absence of correlation between the extracted morphological features and phylogenetic distance. Furthermore, we demonstrated the reconstruction of missing segments from incomplete images. The proposed method provides a flexible and promising tool for analyzing a wide variety of image data of biological shapes even those with missing segments." @default.
• W4383301751 created "2023-07-07" @default.
• W4383301751 creator A5031861661 @default.
• W4383301751 creator A5040545154 @default.
• W4383301751 creator A5070683144 @default.
• W4383301751 creator A5084886439 @default.
• W4383301751 date "2023-07-06" @default.
• W4383301751 modified "2023-09-30" @default.
• W4383301751 title "A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to mandible shape" @default.
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• W4383301751 doi "https://doi.org/10.1038/s41540-023-00293-6" @default.
• W4383301751 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37407628" @default.
• W4383301751 hasPublicationYear "2023" @default.
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• W4383301751 hasAuthorship W4383301751A5031861661 @default.
• W4383301751 hasAuthorship W4383301751A5040545154 @default.

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