Fully Automated 3-D Ultrasound Segmentation of the Placenta, Amniotic Fluid, and Fetus for Early Pregnancy Assessment.

Volumetric placental measurement using 3-D ultrasound has proven clinical utility in predicting adverse pregnancy outcomes. However, this metric cannot currently be employed as part of a screening test due to a lack of robust and real-time segmentation tools. We present a multiclass (MC) convolutional neural network (CNN) developed to segment the placenta, amniotic fluid, and fetus. The ground-truth data set consisted of 2093 labeled placental volumes augmented by 300 volumes with placenta, amniotic fluid, and fetus annotated. A two-pathway, hybrid (HB) model using transfer learning, a modified loss function, and exponential average weighting was developed and demonstrated the best performance for placental segmentation (PS), achieving a Dice similarity coefficient (DSC) of 0.84- and 0.38-mm average Hausdorff distances (HDAV). The use of a dual-pathway architecture improved the PS by 0.03 DSC and reduced HDAV by 0.27 mm compared with a naïve MC model. The incorporation of exponential weighting produced a further small improvement in DSC by 0.01 and a reduction of HDAV by 0.44 mm. Per volume inference using the FCNN took 7-8 s. This method should enable clinically relevant morphometric measurements (such as volume and total surface area) to be automatically generated for the placenta, amniotic fluid, and fetus. The ready availability of such metrics makes a population-based screening test for adverse pregnancy outcomes possible.

Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning.

We present a new technique to fully automate the segmentation of an organ from 3D ultrasound (3D-US) volumes, using the placenta as the target organ. Image analysis tools to estimate organ volume do exist but are too time consuming and operator dependant. Fully automating the segmentation process would potentially allow the use of placental volume to screen for increased risk of pregnancy complications. The placenta was segmented from 2,393 first trimester 3D-US volumes using a semiautomated technique. This was quality controlled by three operators to produce the "ground-truth" data set. A fully convolutional neural network (OxNNet) was trained using this ground-truth data set to automatically segment the placenta. OxNNet delivered state-of-the-art automatic segmentation. The effect of training set size on the performance of OxNNet demonstrated the need for large data sets. The clinical utility of placental volume was tested by looking at predictions of small-for-gestational-age babies at term. The receiver-operating characteristics curves demonstrated almost identical results between OxNNet and the ground-truth). Our results demonstrated good similarity to the ground-truth and almost identical clinical results for the prediction of SGA.

Diagnostic accuracy of colposcopy with dynamic spectral imaging for cytology-negative/high-risk HPV positive (failed test of cure) after large loop excision of the transformation zone (LLETZ) of the cervix: Results of the DySIS colposcopy 1 study.

After treatment for cervical intraepithelial neoplasia (CIN), in the UK women who are cytology-negative, high-risk (HR) human papilloma virus (HPV) positive are referred to colposcopy. This pilot study assessed the incidence of residual/recurrent CIN and the diagnostic accuracy of colposcopy with dynamic spectral imaging (DSI) mapping in their detection.This was a prospective service evaluation carried out in a UK National Health Service (NHS) colposcopy clinic. All women, referred with negative cytology/HR-HPV positive result following treatment for CIN from March 2013 until November 2014, who were examined with the DSI digital colposcope were included. We excluded 3 cases because of poor-quality imaging from user errors. Everyday clinical practice was followed. Initial colposcopic impression, DSI map indication, and biopsy site selections were recorded. CIN2+ was considered the primary outcome and CIN of any grade a secondary outcome.A total of 105 women were included of which 5 (4.8%) had CIN2+ histology and 24 (22.9%) had CIN1. Pre-DSI map colposcopy suggested normal/low grade in all 5 of the CIN2+ cases and DSI suggested high-grade (HG) CIN in 4 of the 5 cases. Sensitivity of standard colposcopy for CIN2+ was 0%, improving to 80% with the incorporation of the DSI map.The CIN burden in this population is higher than previously expected. Colposcopic identification of HG CIN appears to improve significantly with DSI in this cohort leading to refinement in patient management. A larger, multicentric prospective study (DySIS colposcopy 2) is planned to confirm these initial findings.

Is Neurofibromatosis Type 1-Noonan Syndrome a Phenotypic Result of Combined Genetic and Epigenetic Factors?

BACKGROUND/AIM: Neurofibromatosis 1-Noonan syndrome (NFNS) presents combined characteristics of both autosomal dominant disorders: NF1 and Noonan syndrome (NS). The genes causing NF1 and NS are located on different chromosomes, making it uncertain whether NFNS is a separate entity as previously suggested, or rather a clinical variation. PATIENTS AND METHODS: We present a four-membered Greek family. The father was diagnosed with familial NF1 and the mother with generalized epilepsy, being under hydantoin treatment since the age of 18 years. Their two male children exhibited NFNS characteristics. RESULTS: The father and his sons shared R1947X mutation in the NF1 gene. The two children with NFNS phenotype presented with NF1 signs inherited from their father and fetal hydantoin syndrome-like phenotype due to exposure to that anticonvulsant during fetal development. CONCLUSION: The NFNS phenotype may be the result of both a genetic factor (mutation in the NF1 gene) and an epigenetic/environmental factor (e.g. hydantoin).

Repetitive complete molar pregnancy in a 54-year-old patient in a time distance of eighteen years from the first incident: case report and mini review.

We report a case of complete hydatidiform mole in a 54-year-old patient referred to Gynecology department of General Hospital of Athens "Laiko", with history of previous molar pregnancy at the age of thirty-six. Our purpose was to indicate the advanced maternal age beside the long recurrence distance of the disease, which was eighteen years after the first molar pregnancy. Our diagnostic approach was through measurement of serum beta-human chorionic gonadotropin (ß-HCG) and pelvic ultrasound evaluation, and the chosen therapeutic approach was abdominal hysterectomy and bilateral salpingoophorectomy.