Cross-Frequency Collaborative Training Network and Dataset for Semi-supervised First Molar Root Canal Segmentation

TL;DR

The document introduces the elsarticle.cls LaTeX class for formatting manuscripts intended for Elsevier journals. It outlines the class architecture, its reliance on standard packages, and its goal of minimizing conflicts with other packages while providing robust front matter handling. It contrasts elsarticle.cls with the older elsart.cls, highlighting improvements such as an article.cls foundation, multiple final-format models, and streamlined support for front matter and theorem-like environments. It also provides installation and usage guidance, including obtaining the source from Elsevier CTAN resources and integrating natbib and hyperref for consistent editorial workflows. Overall, the class aims to standardize formatting and simplify submission processes for researchers preparing Elsevier submissions.

Abstract

Root canal (RC) treatment is a highly delicate and technically complex procedure in clinical practice, heavily influenced by the clinicians' experience and subjective judgment. Deep learning has made significant advancements in the field of computer-aided diagnosis (CAD) because it can provide more objective and accurate diagnostic results. However, its application in RC treatment is still relatively rare, mainly due to the lack of public datasets in this field. To address this issue, in this paper, we established a First Molar Root Canal segmentation dataset called FMRC-2025. Additionally, to alleviate the workload of manual annotation for dentists and fully leverage the unlabeled data, we designed a Cross-Frequency Collaborative training semi-supervised learning (SSL) Network called CFC-Net. It consists of two components: (1) Cross-Frequency Collaborative Mean Teacher (CFC-MT), which introduces two specialized students (SS) and one comprehensive teacher (CT) for collaborative multi-frequency training. The CT and SS are trained on different frequency components while fully integrating multi-frequency knowledge through cross and full frequency consistency supervisions. (2) Uncertainty-guided Cross-Frequency Mix (UCF-Mix) mechanism enables the network to generate high-confidence pseudo-labels while learning to integrate multi-frequency information and maintaining the structural integrity of the targets. Extensive experiments on FMRC-2025 and three public dental datasets demonstrate that CFC-MT is effective for RC segmentation and can also exhibit strong generalizability on other dental segmentation tasks, outperforming state-of-the-art SSL medical image segmentation methods. Codes and dataset will be released.