Inferior Alveolar Nerve Segmentation in CBCT images using
  Connectivity-Based Selective Re-training
Yusheng Liu Rui Xin Tao Yang Lisheng Wang
Inferior Alveolar Nerve (IAN) canal detection in CBCT is an important step in
many dental and maxillofacial surgery applications to prevent irreversible
damage to the nerve during the procedure.The ToothFairy2023 Challenge aims to
establish a 3D maxillofacial dataset consisting of all sparse labels and
partial dense labels, and improve the ability of automatic IAN segmentation. In
this work, in order to avoid the negative impact brought by sparse labeling, we
transform the mixed supervised problem into a semi-supervised problem. Inspired
by self-training via pseudo labeling, we propose a selective re-training
framework based on IAN connectivity. Our method is quantitatively evaluated on
the ToothFairy verification cases, achieving the dice similarity coefficient
(DSC) of 0.7956, and 95\% hausdorff distance (HD95) of 4.4905, and wining the
champion in the competition. Code is available at
https://github.com/GaryNico517/SSL-IAN-Retraining.