ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume.
Amniotic fluid volume (AFV) is a critical indicator of fetal health, traditionally assessed using ultrasound-based methods, which are limited by operator dependency and 2D measurements. While MRI offers superior tissue characterization, it remains underutilized for AFV assessment due to labor-intens...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 39; no. 2; pp. 1202 - 1217 |
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| Main Authors: | , , , , , , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Apr2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=193167809&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 193167809 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2026 vid: 39 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 193167809 190286225 190286251 193167809 193167809 10.1007/s10278-025-01556-w 193167809 ppf: 1202 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: ARANet: Adaptive Resolution Attention Network for Precise MRI-Based Segmentation and Quantification of Fetal Size and Amniotic Fluid Volume. aug: au: Alkhadrawi, Adham M. Peña-Trujillo, Valeria Gallo-Bernal, Sebastian Gee, Michael S. Cobos, Camilo E. Jaimes Kim, Kyungsu Langarica, Saul Kim, Young-Tak Victoria, Teresa Do, Synho affil: https://ror.org/01wspgy28 Department of Molecular Biosciences and Bioengineering, University of Hawaii at Manoa, 96822, Honolulu, HI, USA sug: subj: Deep Learning Magnetic Resonance Imaging Methods Fetal Development Evaluation Amniotic Fluid Prenatal Diagnosis Maternal Health Services Human Germany Male Female Adult Retrospective Design Record Review Academic Medical Centers Gestational Age Oligohydramnios Polyhydramnios Workflow Fetus Adult: 19-44 years Fetus, conception to birth Male Female ab: Amniotic fluid volume (AFV) is a critical indicator of fetal health, traditionally assessed using ultrasound-based methods, which are limited by operator dependency and 2D measurements. While MRI offers superior tissue characterization, it remains underutilized for AFV assessment due to labor-intensive manual segmentations. Pulse sequence variations in MRI can significantly influence image contrast through T1 and T2 weighting and potentially cause signal dropout in certain regions, making automated analysis challenging. To address these challenges, we present ARANet (Adaptive Resolution Attention Network), featuring a novel adaptive resolution attention module that uniquely combines adaptive resolution processing with channel-wise attention mechanisms for precise MRI-based segmentation. In extensive evaluations, ARANet demonstrates superior performance with a Dice score of 0.961 at full brightness, maintaining robust performance (0.909) even at 30% brightness, significantly outperforming existing models in challenging low-contrast conditions. We introduce FAFO3D, a new metric integrating amniotic fluid overlap, fetal age, and volume, which achieves 100% accuracy in clinical AFV classification (normal, oligohydramnios, and polyhydramnios) when combined with a support vector classifier. A comprehensive web platform automates the workflow from DICOM processing to analysis, providing detailed reports and 3D visualizations for clinical implementation. This solution offers a novel, accurate, and clinically applicable approach for AFV assessment, though further validation in diverse clinical environments is recommended. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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