Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.
Deep learning
Fetal growth restriction
Fetal weight
Growth chart
Magnetic resonance imaging
Journal
European radiology
ISSN: 1432-1084
Titre abrégé: Eur Radiol
Pays: Germany
ID NLM: 9114774
Informations de publication
Date de publication:
02 Sep 2023
02 Sep 2023
Historique:
received:
28
02
2023
accepted:
19
06
2023
revised:
13
06
2023
medline:
4
9
2023
pubmed:
4
9
2023
entrez:
2
9
2023
Statut:
aheadofprint
Résumé
To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity to detect fetuses with growth restriction (FGR). Retrospective data of 348 fetuses with gestational age (GA) of 19-39 weeks were included: 249 normal appropriate for GA (AGA), 19 FGR, and 80 Other (having various imaging abnormalities). A fetal whole-body segmentation model with a quality estimation module was developed and evaluated in 169 cases. The method was evaluated for its repeatability (repeated scans within the same scanner, n = 22), reproducibility (different scanners, n = 6), and accuracy (compared with birth weight, n = 7). A normal MRI-based growth chart was derived. The method achieved a Dice = 0.973, absolute volume difference ratio (VDR) = 1.8% and VDR mean difference = 0.75% ([Formula: see text]: - 3.95%, 5.46), and high agreement with the gold standard. The method achieved a repeatability coefficient = 4.01%, ICC = 0.99, high reproducibility with a mean difference = 2.21% ([Formula: see text]: - 1.92%, 6.35%), and high accuracy with a mean difference between estimated fetal weight (EFW) and birth weight of - 0.39% ([Formula: see text]: - 8.23%, 7.45%). A normal growth chart (n = 246) was consistent with four ultrasound charts. EFW based on MRI correctly predicted birth-weight percentiles for all 18 fetuses ≤ 10 The proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses. Results from this study support the use of the automatic fetal weight estimation method based on MRI for the assessment of fetal development and to detect fetuses at risk for growth restriction. • An AI-based segmentation method with a quality assessment module for fetal weight estimation based on MRI was developed, achieving high repeatability, reproducibility, and accuracy. • An MRI-based fetal weight growth chart constructed from a large cohort of normal and appropriate gestational-age fetuses is proposed. • The method showed a high sensitivity for the diagnosis of small fetuses suspected of growth restriction.
Identifiants
pubmed: 37658890
doi: 10.1007/s00330-023-10038-y
pii: 10.1007/s00330-023-10038-y
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Subventions
Organisme : Israel Innovation Authority
ID : 63418
Organisme : Israel Innovation Authority
ID : 72126
Informations de copyright
© 2023. The Author(s), under exclusive licence to European Society of Radiology.
Références
Zhang-Rutledge K, Mack LM, Mastrobattista JM, Gandhi M (2018) Significance and outcomes of fetal growth restriction below the 5th percentile compared to the 5th to 10th percentiles on midgestation growth ultrasonography. J Ultrasound Med 37(9):2243–2249. https://doi.org/10.1002/jum.14577
doi: 10.1002/jum.14577
pubmed: 29476559
Rizzo G, Mappa I, Bitsadze V et al (2020) Role of Doppler ultrasound at time of diagnosis of late-onset fetal growth restriction in predicting adverse perinatal outcome: prospective cohort study. Ultrasound Obstet Gynecol 55(6):793–798. https://doi.org/10.1002/uog.20406
doi: 10.1002/uog.20406
pubmed: 31343783
Peleg D, Kennedy CM, Hunter SK (1998) Intrauterine growth restriction: identification and management. Am Fam Physician 58(2):453
pubmed: 9713399
Milner J, Jane A (2018) The accuracy of ultrasound estimation of fetal weight in comparison to birth weight: a systematic review. Ultrasound 26(1):32–41. https://doi.org/10.1177/1742271X17732807
doi: 10.1177/1742271X17732807
pubmed: 29456580
pmcid: 5810856
Kacem Y, Cannie MM, Kadji C et al (2013) Fetal weight estimation: comparison of two-dimensional US and MR imaging assessments. Radiology 267(3):902–910. https://doi.org/10.1148/radiol.12121374
doi: 10.1148/radiol.12121374
pubmed: 23329652
Sánchez-Fernández M, Corral ME, Aceituno L, Mazheika M, Mendoza N, Mozas-Moreno J (2021) Observer influence with other variables on the accuracy of Ultrasound estimation of fetal weight at term. Medicina (Kaunas) 57(3):216. https://doi.org/10.3390/medicina57030216
Davidson JR, Uus A, Matthew J et al (2021) Fetal body MRI and its application to fetal and neonatal treatment: an illustrative review. Lancet Child Adolesc Health 5(6):447–458. https://doi.org/10.1016/S2352-4642(20)30313-8
doi: 10.1016/S2352-4642(20)30313-8
pubmed: 33721554
pmcid: 7614154
Gholipour A, Estroff JA, Barnewolt CE et al (2014) Fetal MRI: a technical update with educational aspirations. Concepts Magn Reson Part A Bridg Educ Res 43(6):237–266. https://doi.org/10.1002/cmr.a.21321
Uotila J, Dastidar P, Heinonen T, Ryymin P, Punnonen R, Laasonen E (2000) Magnetic resonance imaging compared to ultrasonography in fetal weight and volume estimation in diabetic and normal pregnancy. Acta Obstet Gynecol Scand 79(4):255–259. https://doi.org/10.1034/j.1600-0412.2000.079004255.x
doi: 10.1034/j.1600-0412.2000.079004255.x
pubmed: 10746838
Zaretsky MV, Reichel TF, McIntire DD, Twickler DM (2003) Comparison of magnetic resonance imaging to ultrasound in the estimation of birth weight at term. Am J Obstet Gynecol 189(4):1017–1020. https://doi.org/10.1067/S0002-9378(03)00895-0
doi: 10.1067/S0002-9378(03)00895-0
pubmed: 14586347
Kadji C, Cannie MM, Resta S et al (2019) Magnetic resonance imaging for prenatal estimation of birthweight in pregnancy: review of available data, techniques, and future perspectives. Am J Obstet Gynecol 220(5):428–439. https://doi.org/10.1016/j.ajog.2018.12.031
doi: 10.1016/j.ajog.2018.12.031
pubmed: 30582928
Torrents-Barrena J, Piella G, Masoller N et al (2019) Segmentation and classification in MRI and US fetal imaging: recent trends and future prospects. Med Image Anal 1(51):61–88. https://doi.org/10.1016/j.media.2018.10.003
doi: 10.1016/j.media.2018.10.003
Anquez J, Bibin L, Angelini ED, Bloch I (2010) Segmentation of the fetal envelope on ante-natal MRI. Proc IEEE Int Symposium on Biomedical Imaging, pp 896–899. https://doi.org/10.1109/ISBI.2010.5490131
Zhang T, Matthew J, Lohezic M et al (2016) Graph-based whole body segmentation in fetal MR images. Proc MICCAI Work PIPPI, Athens, Greece 21:21
Dudovitch G, Link-Sourani D, Ben Sira L, Miller E, Ben Bashat D, Joskowicz L (2020) Deep learning automatic fetal structures segmentation in MRI scans with few annotated datasets. In Proc. International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, pp. 365–374. https://doi.org/10.1007/978-3-030-59725-2_35
Lo J, Nithiyanantham S, Cardinell J et al (2021) Cross Attention Squeeze Excitation Network (CASE-Net) for whole body fetal MRI segmentation. Sensors (Basel) 21(13):4490. https://doi.org/10.3390/s21134490
Ryd D, Nilsson A, Heiberg E, Hedstrom E (2022) Automatic segmentation of the fetus in 3D magnetic resonance images using deep learning: accurate and fast fetal volume quantification for clinical use. Pediatr Cardiol. https://doi.org/10.1007/s00246-022-03038-0
doi: 10.1007/s00246-022-03038-0
pubmed: 36334112
pmcid: 10293340
Bartlett JW, Frost C (2008) Reliability, repeatability and reproducibility: analysis of measurement errors in continuous variables. Ultrasound Obstet Gynecol 31(4):466–475. https://doi.org/10.1002/uog.5256
Specktor-Fadida B, Link-Sourani D, Ferster-Kveller S et al (2021) A bootstrap self-training method for sequence transfer: state-of-the-art placenta segmentation in fetal MRI. In Proc. Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis. Springer, Cham, pp 189–199. https://doi.org/10.1007/978-3-030-87735-4_18
Isensee F, Jaeger PF, Kohl SA, Petersen J, Maier-Hein KH (2021) nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat Methods 18(2):203–211. https://doi.org/10.1038/s41592-020-01008-z
doi: 10.1038/s41592-020-01008-z
pubmed: 33288961
Wang G, Li W, Aertsen M, Deprest J, Ourselin S, Vercauteren T (2019) Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks. Neurocomputing 21(338):34–45. https://doi.org/10.1016/j.neucom.2019.01.103
doi: 10.1016/j.neucom.2019.01.103
Daniel-Spiegel E, Weiner E, Yarom I et al (2013) Establishment of fetal biometric charts using quantile regression analysis. J Ultrasound Med 32(1):23–33. https://doi.org/10.7863/jum.2013.32.1.23
doi: 10.7863/jum.2013.32.1.23
pubmed: 23269707
Hadlock FP, Harrist RB, Martinez-Poyer J (1991) In utero analysis of fetal growth: a sonographic weight standard. Radiology 181(1):129–133. https://doi.org/10.1148/radiology.181.1.1887021
doi: 10.1148/radiology.181.1.1887021
pubmed: 1887021
Kiserud T, Piaggio G, Carroli G et al (2017) The World Health Organization fetal growth charts: a multinational longitudinal study of ultrasound biometric measurements and estimated fetal weight. PLoS Med 14(1):e1002220. https://doi.org/10.1371/journal.pmed.1002220
doi: 10.1371/journal.pmed.1002220
pubmed: 28118360
pmcid: 5261648
Stirnemann J, Villar J, Salomon LJ et al (2017) International estimated fetal weight standards of the INTERGROWTH-21st Project. Ultrasound Obstet Gynecol 49(4):478–486. https://doi.org/10.1002/uog.17347
doi: 10.1002/uog.17347
pubmed: 27804212
pmcid: 5516164
Nicolaides KH, Wright D, Syngelaki A, Wright A, Akolekar R (2018) Fetal Medicine Foundation fetal and neonatal population weight charts. Ultrasound Obstet Gynecol 52(1):44–51. https://doi.org/10.1002/uog.19073
doi: 10.1002/uog.19073
pubmed: 29696704
Dudley NJ (2005) A systematic review of the ultrasound estimation of fetal weight. Ultrasound Obstet Gynecol 25(1):80–89. https://doi.org/10.1002/uog.1751
Raunig DL, McShane LM, Pennello G et al (2015) Quantitative imaging biomarkers: a review of statistical methods for technical performance assessment. Stat Methods Med Res 24(1):27–67. https://doi.org/10.1177/0962280214537344
doi: 10.1177/0962280214537344
pubmed: 24919831
Joskowicz L, Cohen D, Caplan N, Sosna J (2019) Inter-observer variability of manual contour delineation of structures in CT. Eur Radiol 29(3):1391–1399. https://doi.org/10.1007/s00330-018-5695-5
doi: 10.1007/s00330-018-5695-5
pubmed: 30194472
Liao K, Tang L, Peng C et al (2019) A modified model can improve the accuracy of fetal weight estimation by magnetic resonance imaging. Eur J Radiol 1(110):242–248. https://doi.org/10.1016/j.ejrad.2018.12.009
doi: 10.1016/j.ejrad.2018.12.009
Schild RL (2007) Three-dimensional volumetry and fetal weight measurement. Ultrasound Obstet Gynecol 30(6):799–803. https://doi.org/10.1002/uog.5181
doi: 10.1002/uog.5181
pubmed: 17960725
Baker PN, Johnson IR, Gowland PA et al (1994) Fetal weight estimation by echo-planar magnetic resonance imaging. Lancet 343(8898):644–645. https://doi.org/10.1016/S0140-6736(94)92638-7
doi: 10.1016/S0140-6736(94)92638-7
pubmed: 7906814
Shinozuka N, Okai T, Kohzuma S et al (1987) Formulas for fetal weight estimation by ultrasound measurements based on neonatal specific gravities and volumes. Am J Obstet Gynecol 157(5):1140–1145. https://doi.org/10.1016/S0002-9378(87)80278-8
doi: 10.1016/S0002-9378(87)80278-8
pubmed: 3318464
McCrindle B, Zukotynski K, Doyle TE, Noseworthy MD (2021) A radiology-focused review of predictive uncertainty for AI interpretability in computer-assisted segmentation. Radiol Artif Intell 3(6). https://doi.org/10.1148/ryai.2021210031