Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning
Fig 1
Overview and validation of the Phindr3D method.
In a cell segmentation-independent method, Phindr3D iteratively performs two steps on 3D multichannel images. It calculates and categorizes image histograms at different hierarchical levels to compute data-driven image features using unsupervised clustering. Pixels are binned into pixel categories using k-means clustering and supervoxels (SV) are generated by combining pixels from neighboring z-slices. Next, SVs are combined into megavoxels (MV) and MVs are categorized. Each image stack is then defined by the normalized frequency of these different MV categories.