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Research on segmentation of the hippocampus in magnetic resonance images through deep learning convolutional neural networks (CNNs) shows promising results, suggesting that these methods can identify small structural abnormalities of the hippocampus, which are among the earliest and most frequent brain changes associated with Alzheimer disease (AD). However, CNNs typically achieve the highest accuracy on datasets acquired from the same domain as the training dataset. Transfer learning allows domain adaptation through further training on a limited dataset. In this study, we applied transfer learning on a network called spatial warping network segmentation (SWANS), developed and trained in a previous study. We used MR images of patients with clinical diagnoses of mild cognitive impairment (MCI) and AD, segmented by two different raters. By using transfer learning techniques, we developed four new models, using different training methods. Testing was performed using 26% of the original dataset, which was excluded from training as a hold-out test set. In addition, 10% of the overall training dataset was used as a hold-out validation set. Results showed that all the new models achieved better hippocampal segmentation quality than the baseline SWANS model (p s  < .001), with high similarity to the manual segmentations (mean dice [best model] = 0.878 ± 0.003). The best model was chosen based on visual assessment and volume percentage error (VPE). The increased precision in estimating hippocampal volumes allows the detection of small hippocampal abnormalities already present in the MCI phase (SD = [3.9 ± 0.6]%), which may be crucial for early diagnosis., (© 2022 The Authors. Human Brain Mapping published by Wiley Periodicals LLC.)

Objectives: Presumably, changes in fetal circulation contribute to the delay in maturation of the cortex in fetuses with congenital heart defect (CHD). The aim of the current study is to analyze fetal brain development based on hemodynamic differences, using novel brain-age prediction software., Methods: We have performed detailed neurosonography, including acquiring 3D volumes, prospectively in cases with isolated CHD from 20 weeks onwards. An algorithm that assesses the degree of fetal brain-age automatically was used to compare CHD cases to controls. We stratified CHD cases according to flow and oxygenation profiles by lesion physiology and performed subgroup analyses., Results: A total of 616 ultrasound volumes of 162 CHD cases and 75 controls were analyzed. Significant differences in maturation of the cortex were observed in cases with normal blood flow toward the brain (-3.8 days, 95%CI [-5.5; -2.0], P = <.001) and low (-4.0 days, 95% CI [-6.7; -1.2] P = <.05; hypoplastic left heart syndrome[HLHS]) and mixed (-4.4 days, 95%CI [-6.4; -2.5] p = <.001) oxygen saturation in the ascending aorta (TGA) and in cardiac mixing (eg, Fallot) cases., Conclusion: The current study shows significant delay in brain-age in TGA and Fallot cases as compared to control cases. However, the small differences found in this study questions the clinical relevance., (© 2020 The Authors. Prenatal Diagnosis published by John Wiley & Sons Ltd.)

Introduction: Congenital heart defects are associated with neurodevelopmental delay. It is hypothesized that fetuses affected by congenital heart defect have altered cerebral oxygen perfusion and are therefore prone to delay in cortical maturation. The aim of this study was to determine the difference in fetal brain age between consecutive congenital heart defect cases and controls in the second and third trimester using ultrasound., Material and Methods: Since 2014, we have included 90 isolated severe congenital heart defect cases in the Heart And Neurodevelopment (HAND)-study. Every 4 weeks, detailed neurosonography was performed in these fetuses, including the recording of a 3D volume of the fetal brain, from 20 weeks onwards. In all, 75 healthy fetuses underwent the same protocol to serve as a control group. The volumes were analyzed by automated age prediction software which determines gestational age by the assessment of cortical maturation., Results: In total, 477 volumes were analyzed using the age prediction software (199 volumes of 90 congenital heart defect cases; 278 volumes of 75 controls). Of these, 16 (3.2%) volume recordings were excluded because of imaging quality. The age distribution was 19-33 weeks. Mixed model analysis showed that the age predicted by brain maturation was 3 days delayed compared with the control group (P = .002)., Conclusions: This study shows that fetuses with isolated cases of congenital heart defects show some delay in cortical maturation as compared with healthy control cases. The clinical relevance of this small difference is debatable. This finding was consistent throughout pregnancy and did not progress during the third trimester., (© 2019 Nordic Federation of Societies of Obstetrics and Gynecology.)