Automatic Segmentation of the Fetus in 3D Magnetic Resonance Images Using Deep Learning: Accurate and Fast Fetal Volume Quantification for Clinical Use

Magnetic resonance imaging (MRI) provides images for estimating fetal volume and weight, but manual delineations are time consuming. The aims were to (1) validate an algorithm to automatically quantify fetal volume by MRI; (2) compare fetal weight by Hadlock’s formulas to that of MRI; and (3) quanti...

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Vydáno v:Pediatric cardiology Ročník 44; číslo 6; s. 1311 - 1318
Hlavní autoři: Ryd, Daniel, Nilsson, Amanda, Heiberg, Einar, Hedström, Erik
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.08.2023
Springer
Springer Nature B.V
Témata:
Agreements
Algorithms
Amniotic fluid
Cardiac Surgery
Cardiology
Clinical Medicine
Comparative analysis
Coronary vessels
Deep Learning
Engineering and Technology
Fetal Weight
Fetus - diagnostic imaging
Fetuses
Gestational Age
Humans
Klinisk medicin
Magnetic Resonance Imaging
Medical and Health Sciences
Medical Engineering
Medical Imaging
Medicin och hälsovetenskap
Medicine
Medicine & Public Health
Medicinsk bildvetenskap
Medicinteknik
Neural networks
Physiological aspects
Placenta
Pregnancy
R&D
Radiologi och bildbehandling
Radiology and Medical Imaging
Research & development
Teknik
Three dimensional imaging
Ultrasonic imaging
Umbilical cord
Vascular Surgery
Veins & arteries
Sweden
Fetal weight
Fetal cardiovascular magnetic resonance imaging
Prenatal diagnosis
ISSN:0172-0643, 1432-1971, 1432-1971
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Shrnutí:Magnetic resonance imaging (MRI) provides images for estimating fetal volume and weight, but manual delineations are time consuming. The aims were to (1) validate an algorithm to automatically quantify fetal volume by MRI; (2) compare fetal weight by Hadlock’s formulas to that of MRI; and (3) quantify fetal blood flow and index flow to fetal weight by MRI. Forty-two fetuses at 36 (29–39) weeks gestation underwent MRI. A neural network was trained to segment the fetus, with 20 datasets for training and validation, and 22 for testing. Hadlock’s formulas 1–4 with biometric parameters from MRI were compared with weight by MRI. Blood flow was measured using phase-contrast MRI and indexed to fetal weight. Bland–Altman analysis assessed the agreement between automatic and manual fetal segmentation and the agreement between Hadlock’s formulas and fetal segmentation for fetal weight. Bias and 95% limits of agreement were for automatic versus manual measurements 4.5 ± 351 ml (0.01% ± 11%), and for Hadlock 1–4 vs MRI 108 ± 435 g (3% ± 14%), 211 ± 468 g (7% ± 15%), 106 ± 425 g (4% ± 14%), and 179 ± 472 g (6% ± 15%), respectively. Umbilical venous flow was 406 (range 151–650) ml/min (indexed 162 (range 52–220) ml/min/kg), and descending aortic flow was 763 (range 481–1160) ml/min (indexed 276 (range 189–386) ml/min/kg). The automatic method showed good agreement with manual measurements and saves considerable analysis time. Hadlock 1–4 generally agree with MRI. This study also illustrates the confounding effects of fetal weight on absolute blood flow, and emphasizes the benefit of indexed measurements for physiological assessment.
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ISSN:0172-0643
1432-1971
1432-1971
DOI:10.1007/s00246-022-03038-0