| Naslov: | Cephalometric landmark detection in lateral skull X-ray images by using improved spatialconfiguration-net |
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| Avtorji: | ID Šavc, Martin (Avtor)
ID Sedej, Gašper (Avtor)
ID Potočnik, Božidar (Avtor) |
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| Datoteke: |  applsci-12-04644-v2.pdf (2,46 MB)
MD5: CE53D2E64B44D1803FCDE6EEB34C63E6
 https://www.mdpi.com/2076-3417/12/9/4644
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Članek v reviji |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
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| Opis: | 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. |
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| Ključne besede: | detection of cephalometric landmarks, skull X-ray images, convolutional neural networks, deep learning, SpatialConfiguration-Net architecture, AUDAX database |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 14.04.2022 |
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| Datum sprejetja članka: | 29.04.2022 |
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| Datum objave: | 05.05.2022 |
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| Založnik: | MDPI AG |
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| Leto izida: | 2022 |
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| Št. strani: | 21 str. |
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| Številčenje: | Vol. 12, iss. 9 |
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| PID: | 20.500.12556/DKUM-92291  |
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| UDK: | 004.8 |
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| COBISS.SI-ID: | 106866179 |
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| DOI: | 10.3390/app12094644 |
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| ISSN pri članku: | 2076-3417 |
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| Avtorske pravice: | © 2022 by the authors |
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| Datum objave v DKUM: | 27.03.2025 |
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| Število ogledov: | 0 |
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| Število prenosov: | 5 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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