Self-CephaloNet: a two-stage novel framework using operational neural network for cephalometric analysis
<p dir="ltr">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 cons...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , , , , , , , , |
| منشور في: |
2025
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إضافة وسم
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| الملخص: | <p dir="ltr">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 performances have been observed. To address this critical issue, we propose an end-to-end cascaded deep learning framework (Self-CephaloNet) for the task, which demonstrates benchmark performance over the ISBI 2015 dataset in predicting 19 cephalometric landmarks. Due to their adaptive nodal capabilities, Self-ONN (self-operational neural networks) demonstrates superior learning performance for complex feature spaces over conventional convolutional neural networks. To leverage this attribute, we introduce a novel self-bottleneck in the HRNetV2 (high-resolution network) backbone, which has exhibited benchmark performance on our landmark detection task. Our first-stage result surpasses previous studies, showcasing the efficacy of our singular end-to-end deep learning model, which achieves a remarkable 70.95% success rate in detecting cephalometric landmarks within a 2-mm range for the Test1 and Test2 datasets which are part of ISBI 2015 dataset. Moreover, the second stage significantly improves overall performance, yielding an impressive 82.25% average success rate for the datasets above within the same 2-mm distance. Furthermore, external validation has been conducted using the PKU cephalogram dataset. Our model demonstrates a commendable success rate of 75.95% within the 2-mm range.</p><h2>Other Information</h2><p dir="ltr">Published in: Neural Computing and Applications<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1007/s00521-025-11097-6" target="_blank">https://dx.doi.org/10.1007/s00521-025-11097-6</a></p> |
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