A review of image processing methods for fetal head and brain analysis in ultrasound images

Background and objective: Examination of head shape and brain during the fetal period is paramount to evaluate head growth, predict neurodevelopment, and to diagnose fetal abnormalities. Prenatal ultrasound is the most used imaging modality to perform this evaluation. However, manual interpretation of these images is challenging and thus, image processing methods to aid this task have been proposed in the literature. This article aims to present a review of these state-of-the-art methods. Methods: In this work, it is intended to analyze and categorize the different image processing methods to evaluate fetal head and brain in ultrasound imaging. For that, a total of 109 articles published since 2010 were analyzed. Different applications are covered in this review, namely analysis of head shape and inner structures of the brain, standard clinical planes identification, fetal development analysis, and methods for image processing enhancement. Results: For each application, the reviewed techniques are categorized according to their theoretical approach, and the more suitable image processing methods to accurately analyze the head and brain are identified. Furthermore, future research needs are discussed. Finally, topics whose research is lacking in the literature are outlined, along with new fields of applications. Conclusions: A multitude of image processing methods has been proposed for fetal head and brain analysis. Summarily, techniques from different categories showed their potential to improve clinical practice. Nevertheless, further research must be conducted to potentiate the current methods, especially for 3D imaging analysis and acquisition and for abnormality detection. (c) 2022 Elsevier B.V. All rights reserved.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020)This work was funded by projects “NORTE-01–0145-FEDER- 0 0 0 059 , NORTE-01-0145-FEDER-024300 and “NORTE-01–0145- FEDER-0 0 0 045 , supported by Northern Portugal Regional Opera- tional Programme (Norte2020), under the Portugal 2020 Partner- ship Agreement, through the European Regional Development Fund (FEDER). It was also funded by national funds, through the FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by FCT and FCT/MCTES in the scope of the projects UIDB/05549/2020 and UIDP/05549/2020 . The authors also acknowledge support from FCT and the Euro- pean Social Found, through Programa Operacional Capital Humano (POCH), in the scope of the PhD grant SFRH/BD/136670/2018 and SFRH/BD/136721/2018

Fetal head circumference delineation using convolutional neural networks with registration-based ellipse fitting

Examination of head shape during the fetal period is an important task to evaluate head growth and to diagnose fetal abnormalities. Traditional clinical practice frequently relies on the estimation of head circumference (HC) from 2D ultrasound (US) images by manually fitting an ellipse to the fetal skull. However, this process tends to be prone to observer variability, and therefore, automatic approaches for HC delineation can bring added value for clinical practice. In this paper, an automatic method to accurately delineate the fetal head in US images is proposed. The proposed method is divided into two stages: (i) head delineation through a regression convolutional neural network (CNN) that estimates a gaussian-like map of the head contour; and (ii) robust ellipse fitting using a registration-based approach that combines the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms. The proposed method was applied to the HC18 Challenge dataset, which contains 999 training and 335 testing images. Experiments showed that the proposed strategy achieved a mean average difference of -0.11 ± 2.67 mm and a Dice coefficient of 97.95 ± 1.12% against manual annotation, outperforming other approaches in the literature. The obtained results showed the effectiveness of the proposed method for HC delineation, suggesting its potential to be used in clinical practice for head shape assessment.FCT - Fundação para a Ciência e a Tecnologia(UIDB/00319/2020

Placenta accreta spectrum disorders—experience of management in a German tertiary perinatal centre

Purpose!#!Placenta accreta spectrum (PAS) disorders can cause major intrapartum haemorrhage. The optimal management approach is not yet defined. We analysed available cases from a tertiary perinatal centre to compare the outcome of different individual management strategies.!##!Methods!#!A monocentric retrospective analysis was performed in patients with clinically confirmed diagnosis of PAS between 07/2012 and 12/2019. Electronic patient and ultrasound databases were examined for perinatal findings, peripartum morbidity including blood loss and management approaches such as (1) vaginal delivery and curettage, (2) caesarean section with placental removal versus left in situ and (3) planned, immediate or delayed hysterectomy.!##!Results!#!46 cases were identified with an incidence of 2.49 per 1000 births. Median diagnosis of placenta accreta (56%), increta (39%) or percreta (4%) was made in 35 weeks of gestation. Prenatal detection rate was 33% for all cases and 78% for placenta increta. 33% showed an association with placenta praevia, 41% with previous caesarean section and 52% with previous curettage. Caesarean section rate was 65% and hysterectomy rate 39%. In 9% of the cases, the placenta primarily remained in situ. 54% of patients required blood transfusion. Blood loss did not differ between cases with versus without prenatal diagnosis (p = 0.327). In known cases, an attempt to remove the placenta did not show impact on blood loss (p = 0.417).!##!Conclusion!#!PAS should be managed in an optimal setting and with a well-coordinated team. Experience with different approaches should be proven in prospective multicentre studies to prepare recommendations for expected and unexpected need for management

First-Trimester Screening for Neural Tube Defects Using Alpha-Fetoprotein

&lt;i&gt;Objective:&lt;/i&gt; To assess the potential value of maternal serum alpha-fetoprotein (AFP) at 11–13 weeks’ gestation in early screening for fetal neural tube defects (NTDs). &lt;i&gt;Methods:&lt;/i&gt; Maternal serum AFP at 11–13 weeks’ gestation was measured in 32 cases of fetal NTDs, including 18 cases of acrania and 14 cases of spina bifida, and 1,500 unaffected controls. The measured serum AFP was converted into multiple of the expected median (MoM) after adjustment for gestational age and maternal characteristics and Mann-Whitney test was used to determine the significance of difference in the mean MoM of serum AFP in the NTD group to that in the controls. &lt;i&gt;Results:&lt;/i&gt; The mean AFP MoM in the NTD group (1.76, 95% CI 1.39–2.23) was significantly higher than in the controls (p &lt; 0.0001). The mean AFP MoM was not significantly different between the cases of acrania and cases of spina bifida (1.78 vs. 1.75; p = 0.722). The detection rates of NTD in screening by serum AFP were 50.0% (95% CI 31.9–68.1) and 37.5% (95% CI 21.1–56.3) at fixed false-positive rates of 10 and 5%, respectively. &lt;i&gt;Conclusion:&lt;/i&gt; Measurement ofmaternal serum AFP at 11–13 weeks’ gestation may be useful in screening for fetal NTDs.</jats:p