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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) · hlm. 4137–4150

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

Abstrak

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.

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  • Sriwijaya University
  • Politeknik Negeri Sriwijaya
  • University of Indonesia
  • Bandung Institute of Technology
  • Universitas Islam Negeri Imam Bonjol Padang
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Rekan penulisOrganisasiArtikel bersama
Annisa DarmawahyuniSriwijaya University7
Bambang TutukoSriwijaya University6
Muhammad Naufal RachmatullahSriwijaya University6
Ade Iriani SapitriSriwijaya University5
Firdaus FirdausSriwijaya University3
Anggun IslamiSriwijaya University3
Nyayu Latifah HusniBandung Institute of Technology2
Irsyadi YaniSriwijaya University2
Ade SilviaBandung Institute of Technology2
Akhiar Wista ArumSriwijaya University2
Bhakti Yudho SupraptoSriwijaya University2
Tresna DewiPoliteknik Negeri Sriwijaya1
Pola RismaPoliteknik Negeri Sriwijaya1
Yurni OktarinaPoliteknik Negeri Sriwijaya1
Muhammad RorizPoliteknik Negeri Sriwijaya1
Ghita AthalinaAfiliasi tidak diketahui1
Muhammad Alpim Alfa RolisAfiliasi tidak diketahui1
Firdaus FirdausUniversitas Islam Negeri Imam Bonjol Padang1
Erliza YuniartiSriwijaya University1
Samsuryadi SamsuryadiSriwijaya University1
Sutarno SutarnoSriwijaya University1
Dian Palupi RiniSriwijaya University1
Renny Amalia PratiwiSriwijaya University1
Sukemi SukemiSriwijaya University1
Nuswil BernolianAfiliasi tidak diketahui1
Deris StiawanSriwijaya University1
Ahmad ZarkasiSriwijaya University1
Nurul AfifahSriwijaya University1
Winda Kurnia SariSriwijaya University1
Jordan MarcelinoSriwijaya University1
Rendy IsdwantaSriwijaya University1
Cholidah Zuhroh KhairunnisaSriwijaya University1
Ratna DjuwitaUniversity of Indonesia1
Asri Chasanah AdisasmitaUniversity of Indonesia1
Nurhayati Adnan PrihartonoUniversity of Indonesia1
Fara FauziaUniversity of Indonesia1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (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>