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Naslov:Reference areas selection affects registration of AI-segmented mandibles acquired with CBCT
Avtorji:ID Leopold, Klemen (Avtor)
ID Fidler, Aleš (Avtor)
ID Selmani-Bukleta, Manushaqe (Avtor)
ID Kuhar, Milan (Avtor)
Datoteke:.pdf PDF - Predstavitvena datoteka, prenos (1,49 MB)
MD5: 9F1997899782F9119BBF653D4F963C93
 
URL URL - Izvorni URL, za dostop obiščite https://ias-iss.org/ojs/IAS/article/view/3289
 
Jezik:Angleški jezik
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:Logo UKC LJ - Univerzitetni klinični center Ljubljana
Povzetek:Precise registration of sequential 3D datasets is crucial for accurate dimensional analysis. Utilizing the Local Best-Fit (LBF) algorithm and stable Registration Reference Areas (RRAs) facilitates the accurate alignment of 3D surface models. Currently, Cone-beam Computed Tomography (CBCT) and Deep Learn-ing (DL) algorithms are at the forefront for segmenting CBCT scans to monitor morphological changes in the residual alveolar ridge. This study compares the effectiveness of different RRAs in registration sequen-tial 3D surface models of partially edentulous mandibles. DL-assisted software segmented two sequential CBCTs (T0 and T1) from 10 patients, producing sequential 3D mandibular models. These models were aligned using three distinct RRAs: (i) WHOLE, encompassing the entire surface model; (ii) MND_BODY, covering the mandibular body while excluding the unstable alveolar ridge; and (iii) SPIN_FOR, incorpo-rating stable RRAs (mental foramina and mental spine). An innovative method assessed registration accu-racy by generating centroids from cross-sectional outlines of the mandibular nerve canals at the anterior third (A), medial third (B), and posterior third (C) of the posterior edentulous areas. The distance between centroids at T0 and T1 quantified registration accuracy. The MND_BODY group exhibited superior accu-racy, whereas the SPIN_FOR group showed the least, with accuracy decreasing from A to C, suggesting rotational misalignments. When selecting RRAs, both stability and spatial distribution must be taken into account. For optimal alignment, sequential 3D surface models should use RRAs that are both stable and widely distributed.
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Leto izida:2024
Št. strani:str. 211-219
Številčenje:Vol. 43, no. 3
PID:20.500.12556/DiRROS-24515 Novo okno
UDK:616-073.7:004.8
ISSN pri članku:1580-3139
DOI:10.5566/ias.3289 Novo okno
COBISS.SI-ID:245250307 Novo okno
Opomba:
Datum objave v DiRROS:03.12.2025
Število ogledov:33
Število prenosov:11
Metapodatki:XML DC-XML DC-RDF
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Gradivo je del revije

Naslov:Image analysis & stereology : official journal of the International Society for Stereology
Skrajšan naslov:Image anal. stereol.
Založnik:Društvo za stereologijo in kvantitativno analizo slike, Medicinska fakulteta
ISSN:1580-3139
COBISS.SI-ID:106479104 Novo okno

Gradivo je financirano iz projekta

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P3-0293-2020
Naslov:Parodontalna medicina

Licence

Licenca:CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

Jezik:Slovenski jezik
Naslov:Izbor referenčnih območij vpliva na registracijo z umetno inteligenco segmentiranih mandibul, pridobljenig s CBCT
Ključne besede:računalniško podprta obdelava slik, računalniška tomografija s stožčastim stopom, globoko učenje, zobni modeli, delne proteze, trodimenzionalno slikanje


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