Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

OBJECTIVES: 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). METHODS: 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. RESULTS: 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 ≤ 10thpercentile and for 14 out of 17 FGR fetuses below the 3rd percentile. Six fetuses referred to MRI as AGA were found to be < 3rd percentile. CONCLUSIONS: The proposed method for automatic MRI-based EFW demonstrated high performance and sensitivity to identify FGR fetuses. CLINICAL RELEVANCE STATEMENT: 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. KEY POINTS: • 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.

Computational studies of strain exposures in neonate and mature rat brains during closed head impact.

Traumatic brain injury (TBI) is the most common cause of death in childhood, and the majority of fatal cases are due to motor vehicle accidents, falls, sport-related accidents, and child abuse. Rodents and particularly rats became a commonly used animal model of TBI in childhood as well as in adults, and different techniques are described in the literature to induce the brain injury. However, findings reported in the last decade regarding the increased stiffness of brain tissue in young animals, including rats, are not considered in experimental designs of TBI studies, and this may seriously bias the results when TBI effects are compared across different animal ages. In this study, we determined the strain and stress distributions in neonatal (post-natal-day [PND] 13-17) and mature (PND 43 and 90) rat brains during a closed head injury, using age-specific finite element (FE) models. The FE simulations indicated that for identical cortical displacements, the neonatal brain may be exposed to larger peak stress magnitudes compared with a mature brain due to stiffer tissue properties in the neonate, as well as larger strain magnitudes due to its smaller size. The brain volume subjected to a certain strain level was greater in the neonate brain compared with the adult models for all indentation depths greater than 1 mm. In conclusion, our present findings allow better design of closed head impact experiments which involve an age factor. Additionally, the larger peak stresses and larger strain volumetric exposures observed in the neonatal brain support the hypothesis that the smaller size and stiffer tissue of the infant brain makes it more susceptible to TBI.