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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.10173 |
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| _version_ | 1866912733931241472 |
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| author | Jaheen, Ahmed Hassan, Islam Abouserie, Mohanad Rehab, Abdelaty Elasfar, Adham Elmasry, Knzy El-Dawlatly, Mostafa Eldawlatly, Seif |
| author_facet | Jaheen, Ahmed Hassan, Islam Abouserie, Mohanad Rehab, Abdelaty Elasfar, Adham Elmasry, Knzy El-Dawlatly, Mostafa Eldawlatly, Seif |
| contents | Accurate localization of cephalometric landmarks from 2D lateral skull X-rays is vital for orthodontic diagnosis and treatment. Manual annotation is time-consuming and error-prone, whereas automated approaches often struggle with low contrast and anatomical complexity. This paper introduces CephRes-MHNet, a multi-head residual convolutional network for robust and efficient cephalometric landmark detection. The architecture integrates residual encoding, dual-attention mechanisms, and multi-head decoders to enhance contextual reasoning and anatomical precision. Trained on the Aariz Cephalometric dataset of 1,000 radiographs, CephRes-MHNet achieved a mean radial error (MRE) of 1.23 mm and a success detection rate (SDR) @ 2.0 mm of 85.5%, outperforming all evaluated models. In particular, it exceeded the strongest baseline, the attention-driven AFPF-Net (MRE = 1.25 mm, SDR @ 2.0 mm = 84.1%), while using less than 25% of its parameters. These results demonstrate that CephRes-MHNet attains state-of-the-art accuracy through architectural efficiency, providing a practical solution for real-world orthodontic analysis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_10173 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection Jaheen, Ahmed Hassan, Islam Abouserie, Mohanad Rehab, Abdelaty Elasfar, Adham Elmasry, Knzy El-Dawlatly, Mostafa Eldawlatly, Seif Computer Vision and Pattern Recognition Accurate localization of cephalometric landmarks from 2D lateral skull X-rays is vital for orthodontic diagnosis and treatment. Manual annotation is time-consuming and error-prone, whereas automated approaches often struggle with low contrast and anatomical complexity. This paper introduces CephRes-MHNet, a multi-head residual convolutional network for robust and efficient cephalometric landmark detection. The architecture integrates residual encoding, dual-attention mechanisms, and multi-head decoders to enhance contextual reasoning and anatomical precision. Trained on the Aariz Cephalometric dataset of 1,000 radiographs, CephRes-MHNet achieved a mean radial error (MRE) of 1.23 mm and a success detection rate (SDR) @ 2.0 mm of 85.5%, outperforming all evaluated models. In particular, it exceeded the strongest baseline, the attention-driven AFPF-Net (MRE = 1.25 mm, SDR @ 2.0 mm = 84.1%), while using less than 25% of its parameters. These results demonstrate that CephRes-MHNet attains state-of-the-art accuracy through architectural efficiency, providing a practical solution for real-world orthodontic analysis. |
| title | CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.10173 |