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Multiclass instance segmentation optimization for fetal heart image object interpretation

IAES International Journal of Artificial Intelligence · Vol. 14 No. 5 (2025) · pp. 4137–4150

DOI
10.11591/ijai.v14.i5.pp4137-4150
ISSN
2252-8938, 2089-4872
Published
2025-10-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.

Keywords

Collaboration network of the authors

40 authors · 171 co-authorship links · 8 across different organisations · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Sriwijaya University
  • Politeknik Negeri Sriwijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Annisa DarmawahyuniSriwijaya University7
Bambang TutukoSriwijaya University6
Ade Iriani SapitriSriwijaya University5
Muhammad Naufal RachmatullahSriwijaya University5
Anggun IslamiSriwijaya University3
Firdaus FirdausAffiliation unknown3
Irsyadi YaniSriwijaya University2
Ade SilviaPoliteknik Negeri Sriwijaya2
Akhiar Wista ArumSriwijaya University2
Bhakti Yudho SupraptoSriwijaya University2
Nyayu Latifah HusniPoliteknik Negeri Sriwijaya1
Tresna DewiPoliteknik Negeri Sriwijaya1
Pola RismaPoliteknik Negeri Sriwijaya1
Yurni OktarinaPoliteknik Negeri Sriwijaya1
Muhammad RorizPoliteknik Negeri Sriwijaya1
Firdaus FirdausSriwijaya University1
Ghita AthalinaAffiliation unknown1
Muhammad Alpim Alfa RolisAffiliation unknown1
Zulkhair AliAffiliation unknown1
Yenny Dian AndayaniAffiliation unknown1
Aru Wisaksono SudoyoAffiliation unknown1
Mgs. Irsan SalehAffiliation unknown1
Legiran LegiranAffiliation unknown1
Eddy YuristoAffiliation unknown1
Fadhyl Zuhry LubisAffiliation unknown1
Hermawan SusantoAffiliation unknown1
Hayatun NufusAffiliation unknown1
Andhika RachmanAffiliation unknown1
Lugyanti SukrismanAffiliation unknown1
Rudy HidayatAffiliation unknown1
Evy YunihastutiAffiliation unknown1
Simon SalimAffiliation unknown1
Eka GinanjarAffiliation unknown1
Sally Aman NasutionAffiliation unknown1
Ceva Wicaksono PitoyoAffiliation unknown1
Adityo SusiloAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Multiclass instance segmentation optimization for fetal heart image object interpretation</dc:title>
	<dc:creator>Syaputra, Hadi</dc:creator>
	<dc:creator>Nurmaini, Siti</dc:creator>
	<dc:creator>Partan, Radiyati Umi</dc:creator>
	<dc:creator>Roseno, Muhammad Taufik</dc:creator>
	<dc:subject xml:lang="en-US">Deep Learning</dc:subject>
	<dc:subject xml:lang="en-US">Artificial intelligence; Fetal heart; Instance segmentation; Multiclass; ResNet</dc:subject>
	<dc:description xml:lang="en-US">This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-10-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/26793</dc:identifier>
	<dc:identifier>10.11591/ijai.v14.i5.pp4137-4150</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 14, No 5: October 2025; 4137-4150</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v14.i5</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/26793/14804</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Hadi Syaputra, Siti Nurmaini, Radiyati Umi Partan, Muhammad Taufik Roseno</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>