Segment Anything for Dendrites from Electron Microscopy
This work addresses the segmentation of neuronal structures for neuroscience research, with potential applications in computer-assisted diagnosis of brain diseases, but it is incremental as it adapts an existing foundation model to a specific domain.
The authors tackled the problem of segmenting dendrites in electron microscopy images by introducing DendriteSAM, a vision foundation model based on Segment Anything, which achieved better mask quality compared to existing models when tested on diseased rat and human data.
Segmentation of cellular structures in electron microscopy (EM) images is fundamental to analyzing the morphology of neurons and glial cells in the healthy and diseased brain tissue. Current neuronal segmentation applications are based on convolutional neural networks (CNNs) and do not effectively capture global relationships within images. Here, we present DendriteSAM, a vision foundation model based on Segment Anything, for interactive and automatic segmentation of dendrites in EM images. The model is trained on high-resolution EM data from healthy rat hippocampus and is tested on diseased rat and human data. Our evaluation results demonstrate better mask quality compared to the original and other fine-tuned models, leveraging the features learned during training. This study introduces the first implementation of vision foundation models in dendrite segmentation, paving the path for computer-assisted diagnosis of neuronal anomalies.