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Background and Objective: Surgical robotics tends to develop cognitive control architectures to provide certain degree of autonomy to improve patient safety and surgery outcomes, while decreasing the required surgeons' cognitive load dedicated to low level decisions. Cognition needs workspace perception, which is an essential step towards automatic decision-making and task planning capabilities. Robust and accurate detection and tracking in minimally invasive surgery suffers from limited visibility, occlusions, anatomy deformations and camera movements., Method: This paper develops a robust methodology to detect and track anatomical structures in real time to be used in automatic control of robotic systems and augmented reality. The work focuses on the experimental validation in highly challenging surgery: fetoscopic repair of Open Spina Bifida. The proposed method is based on two sequential steps: first, selection of relevant points (contour) using a Convolutional Neural Network and, second, reconstruction of the anatomical shape by means of deformable geometric primitives., Results: The methodology performance was validated with different scenarios. Synthetic scenario tests, designed for extreme validation conditions, demonstrate the safety margin offered by the methodology with respect to the nominal conditions during surgery. Real scenario experiments have demonstrated the validity of the method in terms of accuracy, robustness and computational efficiency., Conclusions: This paper presents a robust anatomical structure detection in present of abrupt camera movements, severe occlusions and deformations. Even though the paper focuses on a case study, Open Spina Bifida, the methodology is applicable in all anatomies which contours can be approximated by geometric primitives. The methodology is designed to provide effective inputs to cognitive robotic control and augmented reality systems that require accurate tracking of sensitive anatomies., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Narcis Sayols reports financial support was provided by Cellex Foundation. Albert Hernansanz reports financial support was provided by Cellex Foundation. Johanna Parra reports financial support was provided by Cellex Foundation. Elisenda Eixarch reports financial support was provided by Cellex Foundation. Eduard Gratacos reports financial support was provided by Cellex Foundation. Alicia Casals reports financial support was provided by Cellex Foundation. Elisenda Eixarch reports financial support was provided by Departament de Salut de la Generalitat de Catalunya., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Background and Objective: The automatic segmentation of perinatal brain structures in magnetic resonance imaging (MRI) is of utmost importance for the study of brain growth and related complications. While different methods exist for adult and pediatric MRI data, there is a lack for automatic tools for the analysis of perinatal imaging., Methods: In this work, a new pipeline for fetal and neonatal segmentation has been developed. We also report the creation of two new fetal atlases, and their use within the pipeline for atlas-based segmentation, based on novel registration methods. The pipeline is also able to extract cortical and pial surfaces and compute features, such as curvature, local gyrification index, sulcal depth, and thickness., Results: Results show that the introduction of the new templates together with our segmentation strategy leads to accurate results when compared to expert annotations, as well as better performances when compared to a reference pipeline (developing Human Connectome Project (dHCP)), for both early and late-onset fetal brains., Conclusions: These findings show the potential of the presented atlases and the whole pipeline for application in both fetal, neonatal, and longitudinal studies, which could lead to dramatic improvements in the understanding of perinatal brain development., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that may occur in pregnancies when two or more fetuses share the same placenta. It is characterized by abnormal vascular connections in the placenta that cause blood to flow unevenly between the babies. If left untreated, perinatal mortality occurs in 90% of cases, whilst neurological injuries are still present in TTTS survivors. Minimally invasive fetoscopic laser surgery is the standard and optimal treatment for this condition, but is technically challenging and can lead to complications. Acquiring and maintaining the required surgical skills need consistent practice, and a steep learning curve. An accurate preoperative planning is thus vital for complex TTTS cases. To this end, we propose the first TTTS fetal surgery planning and simulation platform. The soft tissue of the mother, the uterus, the umbilical cords, the placenta and its vascular tree are segmented and registered automatically from magnetic resonance imaging and 3D ultrasound using computer vision and deep learning techniques. The proposed state-of-the-art technology is integrated into a flexible C++ and MITK-based application to provide a full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, determining the correct entry point, training doctors' movements and trajectory ahead of operation, which allows improving upon current practice. A comprehensive usability study is reported. Experienced surgeons rated highly our TTTS planner and simulator, thus being a potential tool to be implemented in real and complex TTTS surgeries., (Copyright © 2019. Published by Elsevier B.V.)

Objective: To analyze the association between maternal smoking and fetal growth restriction, defined as a failure to achieve the growth potential, and to define subgroups of higher susceptibility for this association., Study Design: A definition of growth restriction by customized birthweight standards applied to 13,661 non-malformed singleton deliveries. Customization was performed by maternal ethnic origin, height, booking weight, parity, gestational age at delivery and fetal gender. The adjusted risk of smoking for customized smallness-for-gestational age and the identification of subgroups with higher susceptibility were assessed by logistic regression., Results: Overall, the adjusted odds ratio of smoking (all levels of exposure grouped) for the occurrence of growth restriction was 1.9 (95% confidence interval: 1.69-2.13). Smoking was etiologically responsible for 13.9% (95% confidence interval: 11.2-16.5) of the cases of growth restriction occurring in the population. Smoking resulted in an increasingly greater risk of growth restriction with progressive levels of cigarette consumption. The risk of smoking for fetal growth restriction was significantly greater in older women and those with a previous history of spontaneous preterm delivery., Conclusions: Smoking is associated with a higher risk for growth restriction. In addition, older pregnant women and those with a previous history of preterm delivery have an increased susceptibility.

Objective: To evaluate the perinatal and neurodevelopmental outcome of small-for-gestational-age fetuses with normal umbilical artery Doppler managed expectantly during pregnancy and delivery., Study Design: Perinatal and neurodevelopmental outcome was assessed from a cohort of singleton small-for-gestational-age fetuses with normal umbilical artery Doppler and normally grown controls matched by gestational age at delivery, parity and parental socio-economic level. Neurodevelopmental outcome was prospectively evaluated by means of the 24-month Age&Stage Questionnaire (ASQ)., Results: A total of 129 small-for-gestational-age fetuses and 259 controls were included. Small-for-gestational-age fetuses had a higher risk for neonatal intensive care unit admission (15.5% versus 3.9%; p<0.001) and significant neonatal morbidity (2.3% versus 0%; p=0.04) than controls. At 24-months, these fetuses showed significantly lower neurodevelopmental centile in the problem solving (42.8 versus 52.1; p=0.001) and personal-social (44.4 versus 54.6; p<0.001) areas than controls., Conclusion: Perinatal and neurodevelopmental outcome in small-for-gestational-age fetuses with normal umbilical artery Doppler is suboptimal, which may challenge the role of umbilical artery Doppler to discriminate between normal-SGA and growth-restricted fetuses.

Objective: To analyse the biological factors affecting birthweight and to derive customized birthweight standards for a Spanish population., Methods: A retrospective cohort was created with all the singleton pregnancies delivered at term and free of pathology in our Institution. Birthweight was modeled by multiple linear regression from maternal (ethnic origin, maternal height, booking weight, smoking, and parity), and fetal (gender, gestational age) characteristics., Results: In addition to gestational age and sex, height, booking weight, ethnic origin, parity, and smoking all have significant and independent effects on birthweight. Women from East-Asia, Morocco and South-America had newborns on average 83 g, 74 g and 95 g heavier than White-European Spanish women. The effect of smoking was found to be dose-related., Conclusion: We found the relative effect of the maternal and fetal characteristics to be very similar to that reported in previous studies. We report coefficients for ethnic groups that account for a sizeable proportion of the population composition of several European countries.