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Title:Deeply-supervised 3D convolutional neural networks for automated ovary and follicle detection from ultrasound volumes
Authors:ID Potočnik, Božidar (Author)
ID Šavc, Martin (Author)
Files:.pdf applsci-12-01246.pdf (1,28 MB)
MD5: 9BB06D60D77966BAF074E78C8B335BCC
 
URL https://www.mdpi.com/2076-3417/12/3/1246
 
Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract: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.
Keywords:3D deep neural networks, 3D ultrasound images of ovaries, deep supervision, detection of follicles and ovaries, U-Net based architecture
Publication status:Published
Publication version:Version of Record
Submitted for review:28.12.2021
Article acceptance date:20.01.2022
Publication date:25.01.2022
Publisher:MDPI AG
Year of publishing:2022
Number of pages:21 str.
Numbering:Vol. 12, iss. 3
PID:20.500.12556/DKUM-92286 New window
UDC:004.5:61
ISSN on article:2076-3417
COBISS.SI-ID:94961923 New window
DOI:10.3390/app12031246 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.
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POTOČNIK, Božidar and ŠAVC, Martin, 2022, Deeply-supervised 3D convolutional neural networks for automated ovary and follicle detection from ultrasound volumes. Applied sciences [online]. 2022. Vol. 12, no. 3. [Accessed 23 April 2025]. DOI 10.3390/app12031246. Retrieved from: https://dk.um.si/IzpisGradiva.php?lang=eng&id=92286
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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:globoke nevronske mreže, ultrazvok jajčnikov, jajčniki


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