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Naslov:Deeply-supervised 3D convolutional neural networks for automated ovary and follicle detection from ultrasound volumes
Avtorji:ID Potočnik, Božidar (Avtor)
ID Šavc, Martin (Avtor)
Datoteke:.pdf applsci-12-01246.pdf (1,28 MB)
MD5: 9BB06D60D77966BAF074E78C8B335BCC
 
URL https://www.mdpi.com/2076-3417/12/3/1246
 
Jezik:Angleški jezik
Vrsta gradiva:Članek v reviji
Tipologija:1.01 - Izvirni znanstveni članek
Organizacija:FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
Opis:Automated detection of ovarian follicles in ultrasound images is much appreciated when its effectiveness is comparable with the experts’ annotations. Today’s best methods estimate follicles notably worse than the experts. This paper describes the development of two-stage deeply-supervised 3D Convolutional Neural Networks (CNN) based on the established U-Net. Either the entire U-Net or specific parts of the U-Net decoder were replicated in order to integrate the prior knowledge into the detection. Methods were trained end-to-end by follicle detection, while transfer learning was employed for ovary detection. The USOVA3D database of annotated ultrasound volumes, with its verification protocol, was used to verify the effectiveness. In follicle detection, the proposed methods estimate follicles up to 2.9% more accurately than the compared methods. With our two-stage CNNs trained by transfer learning, the effectiveness of ovary detection surpasses the up-to-date automated detection methods by about 7.6%. The obtained results demonstrated that our methods estimate follicles only slightly worse than the experts, while the ovaries are detected almost as accurately as by the experts. Statistical analysis of 50 repetitions of CNN model training proved that the training is stable, and that the effectiveness improvements are not only due to random initialisation. Our deeply-supervised 3D CNNs can be adapted easily to other problem domains.
Ključne besede:3D deep neural networks, 3D ultrasound images of ovaries, deep supervision, detection of follicles and ovaries, U-Net based architecture
Status publikacije:Objavljeno
Verzija publikacije:Objavljena publikacija
Poslano v recenzijo:28.12.2021
Datum sprejetja članka:20.01.2022
Datum objave:25.01.2022
Založnik:MDPI AG
Leto izida:2022
Št. strani:21 str.
Številčenje:Vol. 12, iss. 3
PID:20.500.12556/DKUM-92286 Novo okno
UDK:004.5:61
COBISS.SI-ID:94961923 Novo okno
DOI:10.3390/app12031246 Novo okno
ISSN pri članku:2076-3417
Avtorske pravice:© 2022 by the authors
Datum objave v DKUM:27.03.2025
Število ogledov:0
Število prenosov:2
Metapodatki:XML DC-XML DC-RDF
Področja:Ostalo
:
POTOČNIK, Božidar in ŠAVC, Martin, 2022, Deeply-supervised 3D convolutional neural networks for automated ovary and follicle detection from ultrasound volumes. Applied sciences [na spletu]. 2022. Vol. 12, no. 3. [Dostopano 13 april 2025]. DOI 10.3390/app12031246. Pridobljeno s: https://dk.um.si/IzpisGradiva.php?lang=slv&id=92286
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Gradivo je del revije

Naslov:Applied sciences
Skrajšan naslov:Appl. sci.
Založnik:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 Novo okno

Gradivo je financirano iz projekta

Financer:ARIS - Javna agencija za znanstvenoraziskovalno in inovacijsko dejavnost Republike Slovenije
Številka projekta:P2-0041-2020
Naslov:Računalniški sistemi, metodologije in inteligentne storitve

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.

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Jezik:Slovenski jezik
Ključne besede:globoke nevronske mreže, ultrazvok jajčnikov, jajčniki


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