Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and longterm risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI. Methods:The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results:Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean đż 1 difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (đ¶đŒ 95 ) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions. Conclusions:The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.
Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.Methods: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (I of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.Conclusions: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.
Background Advanced magnetic resonance imaging (MRI) methods are increasingly being used to assess the human placenta. Yet, the structureâfunction interplay in normal placentas and their associations with pregnancy risks are not fully understood. Purpose To characterize the normal human placental structure (volume and umbilical cord centricity index (CI)) and function (perfusion) exâvivo using MRI, to assess their association with birth weight (BW), and identify imagingâmarkers for placentas at risk for dysfunction. Study Type Prospective. Population Twenty normal term exâvivo placentas. Field Strength/Sequence 3âT/ T1 and T2 weighted (T1W, T2W) turbo spinâecho, threeâdimensional susceptibilityâweighted image, and timeâresolved angiography with interleaved stochastic trajectories (TWIST), during passage of a contrast agent using MRI compatible perfusion system that mimics placental flow. Assessment Placental volume and CI were manually extracted from the T1W images by a fetalâplacental MRI scientist (D.L., 7âyears of experience). Perfusion maps including bolus arrivalâtime and fullâwidth at half maximum were calculated from the TWIST data. Mean values, entropy, and asymmetries were calculated from each perfusion map, relating to both the whole placenta and volumes of interest (VOIs) within the umbilical cord and its daughter blood vessels. Statistical Tests Pearson correlations with correction for multiple comparisons using false discovery rate were performed between structural and functional parameters, and with BW, with Pâ<â0.05 considered significant. Results All placentas were successfully perfused and scanned. Significant correlations were found between whole placenta and VOIs perfusion parameters (mean R = 0.76â±â0.06, range = 0.67â0.89), which were also significantly correlated with CI (mean R = 0.72â±â0.05, range = 0.65â0.79). BW was correlated with placental volume (R = 0.62), but not with CI (P = 0.40). BW was also correlated with local perfusion asymmetry (R = â0.71). Data Conclusion Results demonstrate a gradient of placental function, associated with CI and suggest several exâvivo imagingâmarkers that might indicate an increased risk for placental dysfunction. Level of Evidence 1 Technical Efficacy Stage 1
Normal fetal adipose tissue (AT) development is essential for perinatal well-being. AT, or simply fat, stores energy in the form of lipids. Malnourishment may result in excessive or depleted adiposity. Although previous studies showed a correlation between the amount of AT and perinatal outcome, prenatal assessment of AT is limited by lacking quantitative methods. Using magnetic resonance imaging (MRI), 3D fat-and water-only images of the entire fetus can be obtained from twopoint Dixon images to enable AT lipid quantification. This paper is the first to present a methodology for developing a deep learning (DL) based method for fetal fat segmentation based on Dixon MRI. It optimizes radiologists' manual fetal fat delineation time to produce annotated training dataset. It consists of two steps: 1) model-based semi-automatic fetal fat segmentations, reviewed and corrected by a radiologist; 2) automatic fetal fat segmentation using DL networks trained on the resulting annotated dataset. Segmentation of 51 fetuses was performed with the semi-automatic method. Three DL networks were trained. We show a significant improvement in segmentation times (3:38 hours â < 1 hour) and observer variability (Dice of 0.738 â 0.906) compared to manual segmentation. Automatic segmentation of 24 test cases with the 3D Residual U-Net, nn-UNet and SWIN-UNetR transformer networks yields a mean Dice score of 0.863, 0.787 and 0.856, respectively. These results are better than the manual observer variability, and comparable to automatic adult and pediatric fat segmentation. A radiologist reviewed and corrected six new independent cases segmented using the best performing network (3D Residual U-Net), resulting in a Dice score of 0.961 and a significantly reduced correction time of 15:20 minutes. Using these novel segmentation methods and short MRI acquisition time, whole body subcutaneous lipids can be quantified for individual fetuses in the clinic and large-cohort research.
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