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arXiv Computer Vision By Zewen Zhuo, Ilya Belevich, Eija Jokitalo, Alejandra Sierra, Jussi Tohka

A Fully Automatic Pipeline for 3D Dendrite Instance Segmentation in SBF-SEM

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The paper introduces a fully automatic pipeline for 3D dendrite instance segmentation in serial block-face scanning electron microscopy (SBF-SEM). It combines YOLOv6-guided Segment Anything Model prompting, iterative 2D mask refinement, random forest 3D linking, and high‑resolution nnU‑Net refinement to produce coherent, well‑separated dendrite reconstructions without manual prompting. Applied to hippocampal CA1 data from a control and an epileptic rat, the method achieves high semantic accuracy (Dice 0.93/0.91) and strong instance performance, while highlighting dense‑region recognition as the main challenge in epileptic tissue.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

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