Automatic linear measurements of the fetal brain on MRI with deep neural networks

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. 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. Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean $L_1$ difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval ($CI_{95}$) of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability. 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.Comment: 15 pages, 8 figures, presented in CARS 2020, submitted to IJCAR

Fetal Brain Tissue Annotation and Segmentation Challenge Results

In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.Comment: Results from FeTA Challenge 2021, held at MICCAI; Manuscript submitte

BNT162b2 Booster Vaccination Induced Immunity against SARS-CoV-2 Variants among Hemodialysis Patients

Background: The emergence of new SARS-CoV-2 variants, which evade immunity, has raised the urgent need for multiple vaccine booster doses for vulnerable populations. In this study, we aimed to estimate the BNT162b2 booster effectiveness against the spread of coronavirus variants in a hemodialysis population. Methods: We compared humoral and cell-mediated immunity in 100 dialysis patients and 66 age-matched volunteers, before and 2–3 weeks following the first booster vaccine dose. Participants were assessed for anti-spike (RBD) antibody titer, neutralizing antibodies against B.1.617.2 (Delta) and B.1.1.529 (Omicron) variants, spike-specific T-cell responses by FACS and infection outbreak after the first and second booster. Results: Anti-spike antibody titer was significantly increased following the booster, with reduced humoral and cellular response in the dialysis patients. Neutralizing antibody levels increased significantly after the booster dose, with an inferior effect (≤2 fold) against Omicron compared with the Delta variant. Furthermore, CD4+ and CD8+ T-cell activation by Delta spike protein was preserved in 70% of PBMCs from the dialysis patients. A second booster dose tended to reduce breakthrough infections in the dialysis patients. Conclusions: Until the release of an updated vaccine, BNT162b2 booster doses will improve the humoral and cell-mediated immunity against variants. These findings support the importance of repetitive booster doses for hemodialysis patients

Extensive elimination of acinar cells during normal postnatal pancreas growth

Summary: While programmed cell death plays important roles during morphogenetic stages of development, post-differentiation organ growth is considered an efficient process whereby cell proliferation increases cell number. Here we demonstrate that early postnatal growth of the pancreas unexpectedly involves massive acinar cell elimination. Measurements of cell proliferation and death in the human pancreas in comparison to the actual increase in cell number predict daily elimination of 0.7% of cells, offsetting 88% of cell formation over the first year of life. Using mouse models, we show that death is associated with mitosis, through a failure of dividing cells to generate two viable daughters. In p53-deficient mice, acinar cell death and proliferation are reduced, while organ size is normal, suggesting that p53-dependent developmental apoptosis triggers compensatory proliferation. We propose that excess cell turnover during growth of the pancreas, and presumably other organs, facilitates robustness to perturbations and supports maintenance of tissue architecture

Carryover Effects of Acute DEHP Exposure on Ovarian Function and Oocyte Developmental Competence in Lactating Cows

<div><p>We examined acute exposure of Holstein cows to di(2-ethylhexyl) phthalate (DEHP) and its carryover effects on ovarian function and oocyte developmental competence. Synchronized cows were tube-fed with water or 100 mg/kg DEHP per day for 3 days. Blood, urine and milk samples were collected before, during and after DEHP exposure to examine its clearance pattern. Ovarian follicular dynamics was monitored through an entire estrous cycle by ultrasonographic scanning. Follicular fluids were aspirated from the preovulatory follicles on days 0 and 29 of the experiment and analyzed for phthalate metabolites and estradiol concentration. The aspirated follicular fluid was used as maturation medium for in-vitro embryo production. Findings revealed that DEHP impairs the pattern of follicular development, with a prominent effect on dominant follicles. The diameter and growth rate of the first- and second-wave dominant follicles were lower (<i>P</i> < 0.05) in the DEHP-treated group. Estradiol concentration in the follicular fluid was lower in the DEHP-treated group than in controls, and associated with a higher number of follicular pathologies (follicle diameter >25 mm). The pattern of growth and regression of the corpus luteum differed between groups, with a lower volume in the DEHP-treated group (<i>P</i> < 0.05). The follicular fluid aspirated from the DEHP-treated group, but not the controls, contained 23 nM mono(2-ethylhexyl) phthalate. Culturing of cumulus oocyte complexes in the follicular fluid aspirated from DEHP-treated cows reduced the proportion of oocytes progressing to the MII stage, and the proportions of 2- to 4-cell-stage embryos (<i>P</i> < 0.04) and 7-day blastocysts (<i>P</i> < 0.06). The results describe the risk associated with acute exposure to DEHP and its deleterious carryover effects on ovarian function, nuclear maturation and oocyte developmental competence.</p></div

Estradiol and phthalate-metabolite concentrations in the follicular fluid (FF).

<p>Cows were tube-fed DEHP or water (control) on days 1–3 of the experiment. FFs of the preovulatory follicles were aspirated before (day 0) and after (day 29) DEHP treatment. Data presented as mean ± SEM; <i>P</i>-value indicates for treatment effect within experimental groups on each examined day. (B) Phthalate-metabolite concentrations in FF on day 0 and day 29 of the experiment. MMP, mono-methyl phthalate; MEP, mono-ethyl phthalate; MBP, mono-n-butyl phthalate; MEHP, mono(2-ethylhexyl) phthalate.</p

DEHP-metabolite concentrations in the plasma before, during and after DEHP exposure.

<p>Cows were administered with water (control) or 100 mg/kg DEHP per day on days 1–3 of the experiment (DEHP-treated). Blood samples were taken before (day 0), during (days 2 and 4) and after (days 11, 19 and 24) DEHP exposure. Samples were analyzed for concentrations of DEHP metabolites mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (5OH-MEHP), mono(2-ethyl-5-oxohexyl) phthalate (5oxo-MEHP), mono(2-ethyl-5-carboxypentyl) phthalate (5cx-MEPP) and mono[2-(carboxymethyl)hexyl] phthalate (2cx-MMHP) by LC–MS/MS.</p

Phthalate-metabolite concentrations (nM) in pooled plasma, milk and urine samples collected before, during and after DEHP exposure.

<p>*Before exposure = samples collected from all cows on day 0 of the experiment.</p><p>**Control = samples collected from control cows on days 2, 4, 11, 19 and 24 of the experiment.</p><p>***During exposure = samples collected from DEHP-treated cows on days 2 and 4 of the experiment.</p><p>****After exposure = samples collected from DEHP-treated cows on days 11, 19 and 24 of the experiment.</p><p>Data presented as mean ± SEM. Different superscript letters indicate significant difference at <i>P</i> < 0.05 within columns, for each sample and metabolite separately.</p><p>Phthalate-metabolite concentrations (nM) in pooled plasma, milk and urine samples collected before, during and after DEHP exposure.</p