bibtext: @InProceedings{ Ren_Selfsupervised_MICCAI2024,
                   author = { Ren, Jiaxiang and Li, Zhenghong and Cheng, Wensheng and Zou, Zhilin and Park, Kicheon and Pan, Yingtian and Ling, Haibin },
                   title = { { Self-supervised 3D Skeleton Completion for Vascular Structures } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: 3D skeleton is critical for analyzing vascular structures with many applications, it is however often limited by the broken skeletons due to image degradation. Existing methods usually correct such skeleton breaks via handcrafted connecting rules or rely on nontrivial manual annotation, which is susceptible to outliers or costly especially for 3D data. In this paper, we propose a self-supervised approach for vasculature reconnection. Specifically, we generate synthetic breaks from confident skeletons and use them to guide the learning of a 3D UNet-like skeleton completion network. To address serious imbalance among different types of skeleton breaks, we introduce three skeleton transformations that largely alleviate such imbalance in synthesized break samples. This allows our model to effectively handle challenging breaks such as bifurcations and tiny fragments. Additionally, to encourage the connectivity outcomes, we design a novel differentiable connectivity loss for further improvement. Experiments on a public medical segmentation benchmark and a 3D optical coherence Doppler tomography (ODT) dataset show the effectiveness of our method.
title: Self-supervised 3D Skeleton Completion for Vascular Structures
authors: Ren, Jiaxiang Li, Zhenghong Cheng, Wensheng Zou, Zhilin Park, Kicheon Pan, Yingtian Ling, Haibin