Publications
Detailed Information
Cephalometric landmark detection via global and local encoders and patch-wise attentions
Cited 3 time in
Web of Science
Cited 6 time in Scopus
- Authors
- Issue Date
- 2022-01-22
- Publisher
- Elsevier BV
- Citation
- Neurocomputing, Vol.470, pp.182-189
- Abstract
- Cephalometric landmark detection performs an important role in a diagnostic measurements for orthodontic treatment plans. As manual depiction of landmarks is a time-consuming and tedious task, the development of an automated detection algorithm for daily clinics is in high demand. In this study, we propose a single-passing convolutional neural network that performs an accurate landmark detection in a hierarchical fashion. The proposed network first extracts global contexts by regressing initial positions of all the landmarks. Subsequently, local features are extracted from landmark-centered patches, which are obtained through global regression. The encoded global and local features are concatenated and weighed through a novel patch-wise attention module to weigh the relative importance. The experimental results demonstrate that our proposed local patch-wise attention mechanism performs a significant role in accurate detection. The proposed method outperformed other state-of-the-art methods by improving the successful detection rate by approximately 1 ti 2%. The proposed method suggests that a structured attention, which is developed in a patch-wise fashion, significantly enhances the local feature encoders to further improve the final accuracy. (c) 2021 Elsevier B.V. All rights reserved.
- ISSN
- 0925-2312
- Files in This Item:
- There are no files associated with this item.
Item View & Download Count
Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.