Segment Anything for Dendrites from Electron Microscopy

• URL: http://arxiv.org/abs/2411.02562v1
• Date: Mon, 04 Nov 2024 19:54:26 GMT
• Title: Segment Anything for Dendrites from Electron Microscopy
• Authors: Zewen Zhuo, Ilya Belevich, Ville Leinonen, Eija Jokitalo, Tarja Malm, Alejandra Sierra, Jussi Tohka,
• Abstract summary: 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 hippocampus rat and is tested on diseased rat and human data.
• Score: 36.25707481854301
• License:
• Abstract: 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.

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