Part segmentation is a crucial task for 3D curvilinear structures like neuron
dendrites and blood vessels, enabling the analysis of dendritic spines and
aneurysms with scientific and clinical significance. However, their diversely
winded morphology poses a generalization challenge to existing deep learning
methods, which leads to labor-intensive manual correction. In this work, we
propose FreSeg, a framework of part segmentation tasks for 3D curvilinear
structures. With Frenet-Frame-based point cloud transformation, it enables the
models to learn more generalizable features and have significant performance
improvements on tasks involving elongated and curvy geometries. We evaluate
FreSeg on 2 datasets: 1) DenSpineEM, an in-house dataset for dendritic spine
segmentation, and 2) IntrA, a public 3D dataset for intracranial aneurysm
segmentation. Further, we will release the DenSpineEM dataset, which includes
roughly 6,000 spines from 69 dendrites from 3 public electron microscopy (EM)
datasets, to foster the development of effective dendritic spine instance
extraction methods and, consequently, large-scale connectivity analysis to
better understand mammalian brains.

通过 Frenet-Frame-based 点云转换，FreSeg 提出了一个面向 3D 曲线结构的部分分割任务的框架，使模型能够学习更通用的特征，并在涉及细长和曲线几何形状的任务上取得了显著的性能改进。