AI-Powered Cephalometric Landmark Detection for Orthodontic Diagnosis: Framework, Dataset, and Detection Challenge

Abstract

Quantitative cephalometry is the most widely used clinical and research tool in modern orthodontics, enabling the quantification and classification of anatomical abnormalities through localization of cephalometric landmarks. The traditional way of marking these landmarks on lateral cephalograms is a monotonous and time-consuming job. Endeav ors have constantly been made to develop automated cephalometric landmark detection systems but they are inadequate for orthodontic applications. The fundamental reason for this is that the amount of publicly available datasets as well as the images provided for training in these datasets are insufficient for an AI model to perform well. Further more, most of the existing approaches rely on sets of network architectures to regress landmark coordinates, which require significant computational resources and can be memory-intensive. These limitations have hindered the clinical adoption of these meth ods, and there is a need for more efficient and practical approaches to cephalometric landmark detection. In this research study, we propose a single, end-to-end trainable, multi-stage regression framework for automatic cephalometric landmark detection. The framework follows a coarse-to-fine detection strategy, which mainly consists of two stages. In the first stage, the initial positions of all landmarks are estimated simultaneously to identify candidate regions that are likely to contain the target landmark. In the second stage, we leverage the multi-resolution feature maps generated by the in-network feature hierarchy to pro duce high-level, semantically rich features. These feature maps are then cropped based on the coarsely detected landmark locations, which allows us to refine the initial land mark estimates. The framework uses a multi-head CNN architecture that shares a single backbone neural network, allowing for the reuse of multi-resolution feature maps from different layers without incurring any additional computational cost. The shared back bone also enables inter-module communication, which helps the modules learn from each xvi List of Tables other’s predictions and align themselves accordingly. The proposed framework achieves state-of-the-art results on a public benchmark dataset, demonstrating the superiority of our approach over existing methods. Furthermore, the current cephalometric datasets available to researchers are limited in their scope and capabilities and to make significant strides in the field of cephalometric analysis, a new and improved dataset is necessary. One that is comprehensive and di verse, with a larger number of cephalograms and more extensive annotations. Taking a cue, we also propose a new state-of-the-art dataset for cephalometric analysis, featuring 1000 cephalometric X-ray images acquired from 7 different imaging devices with varying resolutions. It boasts the most extensive collection of annotated soft tissue landmarks ever included in a publicly available dataset, as well as the first standard resource for CVM classification. Our team of clinical experts studiously annotated each image with 29 commonly used anatomical landmarks in two methodical phases, making it a valu able tool for researchers working to develop AI solutions for morphometric analysis. Lastly, to facilitate the development of robust AI solutions for morphometric analysis, we organize the CEPHA29 Automatic Cephalometric Landmark Detection Challenge in conjunction with IEEE International Symposium on Biomedical Imaging (ISBI 2023). A total of 29 researchers and medical practitioners have participated in this contest and made significant strides towards the development of cutting-edge algorithms to improve the overall accuracy of cephalometric landmark detection within the clinically accepted range of 2mm. In conclusion, this research work has significant implications for automated cephalome try and modern orthodontics, as it provides an effective solution to an otherwise time consuming and tedious process, paving the way for improved orthodontic diagnosis and treatment planning while streamlining clinical workflows.