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Title:Cephalometric landmark detection in lateral skull X-ray images by using improved spatialconfiguration-net
Authors:ID Šavc, Martin (Author)
ID Sedej, Gašper (Author)
ID Potočnik, Božidar (Author)
Files:.pdf applsci-12-04644-v2.pdf (2,46 MB)
MD5: CE53D2E64B44D1803FCDE6EEB34C63E6
 
URL https://www.mdpi.com/2076-3417/12/9/4644
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Accurate automated localization of cephalometric landmarks in skull X-ray images is the basis for planning orthodontic treatments, predicting skull growth, or diagnosing face discrepancies. Such diagnoses require as many landmarks as possible to be detected on cephalograms. Today’s best methods are adapted to detect just 19 landmarks accurately in images varying not too much. This paper describes the development of the SCN-EXT convolutional neural network (CNN), which is designed to localize 72 landmarks in strongly varying images. The proposed method is based on the SpatialConfiguration-Net network, which is upgraded by adding replications of the simpler local appearance and spatial configuration components. The CNN capacity can be increased without increasing the number of free parameters simultaneously by such modification of an architecture. The successfulness of our approach was confirmed experimentally on two datasets. The SCN-EXT method was, with respect to its effectiveness, around 4% behind the state-of-the-art on the small ISBI database with 250 testing images and 19 cephalometric landmarks. On the other hand, our method surpassed the state-of-the-art on the demanding AUDAX database with 4695 highly variable testing images and 72 landmarks statistically significantly by around 3%. Increasing the CNN capacity as proposed is especially important for a small learning set and limited computer resources. Our algorithm is already utilized in orthodontic clinical practice.
Keywords:detection of cephalometric landmarks, skull X-ray images, convolutional neural networks, deep learning, SpatialConfiguration-Net architecture, AUDAX database
Publication status:Published
Publication version:Version of Record
Submitted for review:14.04.2022
Article acceptance date:29.04.2022
Publication date:05.05.2022
Publisher:MDPI AG
Year of publishing:2022
Number of pages:21 str.
Numbering:Vol. 12, iss. 9
PID:20.500.12556/DKUM-92291 New window
UDC:004.8
ISSN on article:2076-3417
COBISS.SI-ID:106866179 New window
DOI:10.3390/app12094644 New window
Copyright:© 2022 by the authors
Publication date in DKUM:27.03.2025
Views:0
Downloads:5
Metadata:XML DC-XML DC-RDF
Categories:Misc.
:
ŠAVC, Martin, SEDEJ, Gašper and POTOČNIK, Božidar, 2022, Cephalometric landmark detection in lateral skull X-ray images by using improved spatialconfiguration-net. Applied sciences [online]. 2022. Vol. 12, no. 9. [Accessed 23 April 2025]. DOI 10.3390/app12094644. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=92291
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
Publisher:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 New window

Document is financed by a project

Funder:ARIS - Slovenian Research and Innovation Agency
Project number:P2-0041-2020
Name:Računalniški sistemi, metodologije in inteligentne storitve

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.

Secondary language

Language:Slovenian
Keywords:slike, konvolucijske nevronske mreže, globoko učenje


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