Graduate School of Sciences, The University of Tokyo, 7-3-1 Hongo, Tokyo, 113-0033, Japan.
Graduate School of Integrated Sciences for Life, Hiroshima University, 1-3-1 Kagamiyama, Higashi-Hiroshima City, Hiroshima, 739-8528, Japan.
NPJ Syst Biol Appl. 2023 Jul 6;9(1):30. doi: 10.1038/s41540-023-00293-6.
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.
形状测量对于进化和发育生物学至关重要；然而，它们在对任意形状进行客观和自动量化方面存在困难。传统方法基于解剖上突出的地标，这需要专家进行手动注释。在这里，我们通过提出基于形态的调节变分自动编码器（Morpho-VAE），即一种基于图像的深度学习框架，来开发一种机器学习方法，以进行无地标形状分析。所提出的架构结合了无监督和监督学习模型，通过专注于区分具有不同标签的数据的形态特征来降低维度。我们将该方法应用于灵长类动物下颌骨图像数据。尽管提取的形态特征与提取的形态特征和系统发育距离之间没有相关性，但提取的形态特征反映了生物体所属家族的特征。此外，我们还展示了从不完整图像中重建缺失片段的能力。该方法为分析各种生物形状的图像数据提供了一种灵活且有前途的工具，即使是那些具有缺失片段的图像数据也可以进行分析。
