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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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A deep learning approach for morphological feature extraction based on variational auto-encoder: an application to

Masato Tsutsumi¹, Nen Saito^2,3,4, Daisuke Koyabu^5,6

¹Graduate School of Sciences, The University of Tokyo, 7-3-1 Hongo, Tokyo, 113-0033, Japan.

NPJ Systems Biology and Applications

|July 5, 2023

Summary

This study introduces Morpho-VAE, a deep learning method for landmark-free biological shape analysis. It accurately quantifies shapes from images, even with missing data, aiding evolutionary and developmental biology research.

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Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

Area of Science:

Evolutionary biology
Developmental biology
Bioinformatics
Computer vision

Background:

Objective quantification of biological shapes is challenging.
Traditional methods rely on manual landmark annotation, limiting scalability and objectivity.
Automated, landmark-free approaches are needed for analyzing complex biological forms.

Purpose of the Study:

To develop an automated, landmark-free deep learning framework for biological shape analysis.
To reduce dimensionality by focusing on key morphological features.
To enable analysis of incomplete or damaged biological shape data.

Main Methods:

Developed Morpho-VAE (morphological regulated variational AutoEncoder), an image-based deep learning framework.
Combined unsupervised and supervised learning for dimensionality reduction.
Applied the framework to primate mandible image data.

Main Results:

Morpho-VAE successfully extracted morphological features distinguishing primate families.
Extracted features reflected taxonomic characteristics, independent of phylogenetic distance.
Demonstrated capability to reconstruct missing segments in incomplete images.

Conclusions:

Morpho-VAE offers a flexible and promising tool for objective, automated analysis of diverse biological shape data.
The method is effective even for images with missing segments, broadening its applicability.
Facilitates advancements in evolutionary and developmental biology research through enhanced shape quantification.