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.
Mechanical events and alterations in neuronal morphology that accompany neuronal activity have been observed for decades. However, no clear neurophysiological role, nor an agreed molecular mechanism relating these events to the electrochemical process, has been found. Here we hypothesized that intense, yet physiological, electrical activity in neurons triggers cytoskeletal depolymerization. We excited the sciatic nerve of anesthetized mice with repetitive electric pulses (5, 10, and 100 Hz) for 1 and 2 min and immediately fixed the excised nerves. We then scanned the excised nerves with high-resolution transmission electron microscopy, and quantified cytoskeletal changes in the resulting micrographs. We demonstrate that excitation with a stimulation frequency that is within the physiological regime is accompanied by a significant reduction in the density of cytoskeletal proteins relative to the baseline values recorded in control nerves. After 10 Hz stimulation with durations of 1 and 2 min, neurofilaments density dropped to 55.8 and 51.1% of the baseline median values, respectively. In the same experiments, microtubules density dropped to 23.7 and 38.5% of the baseline median values, respectively. These changes were also accompanied by a reduction in the cytoskeleton-to-cytoplasm contrast that we attribute to the presence of depolymerized electron-dense molecules in the lumen. Thus, we demonstrate with an in vivo model a link between electrical activity and immediate cytoskeleton rearrangement at the nano-scale. We suggest that this cytoskeletal plasticity reduces cellular stiffness and allows cellular homeostasis, maintenance of neuronal morphology and that it facilitates in later stages growth of the neuronal projections.
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