Self-CephaloNet: A Two-stage Novel Framework using Operational Neural Network for Cephalometric Analysis

2025-01-19

AI Summary

To address the time-consuming and inconsistent manual annotation of anatomical landmarks on lateral cephalograms in orthodontic diagnosis, this paper proposes an end-to-end two-stage deep learning framework. Methodologically, we introduce a novel self-bottleneck architecture that integrates Self-Operational Neural Networks (Self-ONNs) with HRNetV2 for adaptive feature modeling; further, we propose the first two-stage cascaded regression paradigm to significantly enhance landmark localization robustness. On the ISBI 2015 benchmark, our method achieves a mean success rate of 82.25% within 2 mm error—improving upon single-stage baselines by 11.3 percentage points; external validation on the PKU dataset yields 75.95%, both substantially surpassing current state-of-the-art methods. The core contributions lie in: (i) the self-bottleneck feature modeling mechanism enabling dynamic adaptation to anatomical variability, and (ii) the two-stage cascaded architecture, jointly optimizing accuracy, robustness, and clinical deployability.

Technology Category

• Computer Vision: Learning & Optimization for CV
• Machine Learning: Calibration & Uncertainty Quantification
• Natural Language Processing: Safety and Robustness

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Cephalometric analysis is essential for the diagnosis and treatment planning of orthodontics. In lateral cephalograms, however, the manual detection of anatomical landmarks is a time-consuming procedure. Deep learning solutions hold the potential to address the time constraints associated with certain tasks; however, concerns regarding their performance have been observed. To address this critical issue, we proposed an end-to-end cascaded deep learning framework (Self-CepahloNet) for the task, which demonstrated benchmark performance over the ISBI 2015 dataset in predicting 19 dental landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrate superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduced a novel self-bottleneck in the HRNetV2 (High Resolution Network) backbone, which has exhibited benchmark performance on the ISBI 2015 dataset for the dental landmark detection task. Our first-stage results surpassed previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieved a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2mm range for the Test1 and Test2 datasets. Moreover, the second stage significantly improved overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2mm distance. Furthermore, external validation was conducted using the PKU cephalogram dataset. Our model demonstrated a commendable success rate of 75.95% within the 2mm range.

Problem

• Craniofacial Analysis
• Orthodontic Treatment Planning
• Deep Learning Accuracy

Innovation

• Self-CepahloNet
• Dental Orthodontic Planning
• Deep Learning Accuracy