Prospective study of malaria in pregnancy, placental and congenital malaria in Northwest Colombia.

BACKGROUND: Pregnancy Associated Malaria (PAM) include malaria in pregnancy (MiP), placental malaria (PM), and congenital malaria (CM). The evidence available in Colombia on PAM focuses on one of the presentations (MiP, PM or CM), and no study longitudinally analyses the infection from the pregnant woman, passing through the placenta, until culminating in the newborn. This study determined the frequency of MiP, PM, and CM caused by Plasmodium vivax, Plasmodium falciparum, or mixed infections, according to Thick Blood Smear (TBS) and quantitative Polymerase Chain Reaction (qPCR). Identifying associated factors of PAM and clinical-epidemiological outcomes in northwestern Colombia. METHODS: Prospective study of 431 pregnant women, their placenta, and newborns registered in the data bank of the research Group "Salud y Comunidad César Uribe Piedrahíta" which collected information between 2014 and 2020 in endemic municipalities of the departments of Córdoba and Antioquia. The frequency of infection was determined with 95% confidence intervals. Comparisons were made with the Chi-square test, Student t-test, prevalence ratios, and control for confounding variables by log-binomial regression. RESULTS: The frequency of MiP was 22.3% (4.6% using TBS), PM 24.8% (1.4% using TBS), and CM 11.8% (0% using TBS). Using TBS predominated P. vivax. Using qPCR the proportions of P. vivax and P. falciparum were similar for MiP and PM, but P. falciparum predominated in CM. The frequency was higher in nulliparous, and women with previous malaria. The main clinical effects of PAM were anaemia, low birth weight, and abnormal APGAR score. CONCLUSIONS: The magnitude of infections was not detected with TBS because most cases were submicroscopic (TBS-negative, qPCR-positive). This confirmed the importance of improving the molecular detection of cases. PAM continue being underestimated in the country due to that in Colombia the control programme is based on TBS, despite its outcomes on maternal, and congenital health.

Lifestyles associated with malaria in pregnancy in northwest Colombia: a mixed study from Latin American critical epidemiology.

BACKGROUND: In the scientific literature on Malaria in Pregnancy (MiP), no studies have been conducted on lifestyles based on critical theory. The objective of this study was to analyse the lifestyles or singular processes of social determination of health in MiP in northwestern Colombia. METHODS: Mixed QUAN-QUAL convergent triangulation study. In the quantitative component, a psychometric evaluation and a cross-sectional design were conducted in 400 pregnant women to whom the Pender-Walker lifestyle scale and a survey on MiP prevention were applied. In the qualitative study, a critical ethnography was conducted with 46 pregnant women in whom their narratives and practices regarding lifestyles at home and healthcare were described. RESULTS: The frequency of MiP was 9%, and a higher occurrence of the disease was identified in those who did not control stagnant water (29%), did not use insecticide-treated net (16%) and went to the hospital (14%) or the microscopist (20%) when they had fever. This coincides with the presence of unhealthy lifestyles, little knowledge about malaria, and a low perception of the risk of getting sick, as well as meanings and experiences about MiP, maternity, and pregnancy that show a high clinical, cultural, and socioeconomic burden for the women studied. CONCLUSION: This epidemiological profile and the approach to lifestyles based on the postulates of critical theory in health evidence that pregnant women exposed to malaria suffer serious social, cultural and health injustices that are not possible to impact with the current health model of malaria control in Colombia guided by aetiopathogenic, biomedical, positivist and utilitarian theories.

Social determination of malaria in pregnancy in Colombia: a critical ethnographic study.

BACKGROUND: The meanings and experiences related to malaria in pregnancy (MiP) and its processes of social determination of health (PSDH) have not been reported in the world scientific literature. The objective was to understand the meanings and experiences of MiP, and to explain their PSDH in an endemic area from Colombia, 2022. METHODS: Critical ethnography with 46 pregnant women and 31 healthcare workers. In-depth and semi-structured interviews, focus group discussions, participant and non-participant observations, and field diaries were applied. A phenomenological-hermeneutic analysis, saturation and triangulation was carried out. The methodological rigor criteria were reflexivity, credibility, auditability, and transferability. RESULTS: At the singular level, participants indicated different problems in antenatal care and malaria control programmes, pregnant women were lacking knowledge about MiP, and malaria care was restricted to cases with high obstetric risk. Three additional levels that explain the PSDH of MiP were identified: (i) limitations of malaria control policies, and health-system, geographic, cultural and economic barriers by MiP diagnosis and treatment; (ii) problems of public health programmes and antenatal care; (iii) structural problems such as monetary poverty, scarcity of resources for public health and inefficiency in their use, lacking community commitment to preventive actions, and breach of institutional responsibilities of health promoter entity, municipalities and health services provider institutions. CONCLUSION: Initiatives for MiP control are concentrated at the singular level, PDSH identified in this research show the need to broaden the field of action, increase health resources, and improve public health programmes and antenatal care. It is also necessary to impact the reciprocal relationships of MiP with economic and cultural dimensions, although these aspects are increasingly diminished with the predominance and naturalization of neoliberal logic in health.

A systematic review of mixed studies on malaria in Colombia 1980-2022: what the "bifocal vision" discovers.

Mixed methods are essential in public health research and malaria control, because they allow grasping part of the complexity and diversity of the factors that determine health-disease. This study analyzes the mixed studies on malaria in Colombia, 1980-2022, through a systematic review in 15 databases and institutional repositories. The methodological quality was assessed with Mixed Methods Appraisal Tool (MMAT), STrengthening the Reporting of OBservational studies in Epidemiology (STROBE), and Standards for Reporting Qualitative Research (SRQR). The qualitative and quantitative findings were grouped into a four-level hierarchical matrix. The epidemiological profile of malaria morbidity, from traditional epidemiology, has been sustained by environmental problems, armed conflict, individual risk behaviors, and low adherence to recommendations from health institutions. However, the qualitative component reveals deeper causes that are less studied, of greater theoretical complexity, and that reflect challenges to design and implement health interventions, such as socioeconomic and political crises, poverty, and the neoliberal orientation in the malaria control policy; the latter reflected in the change in the role of the State, the fragmentation of control actions, the predominance of insurance over social assistance, the privatization of the provision of health services, the individualistic and economistic predominance of health, and low connection with popular tradition and community initiatives. The above confirms the importance of expanding mixed studies as a source of evidence to improve malaria research and control models in Colombia, and to identify the underlying causes of the epidemiological profile.

Sensor Fusion Approach for Multiple Human Motion Detection for Indoor Surveillance Use-Case.

Multi-human detection and tracking in indoor surveillance is a challenging task due to various factors such as occlusions, illumination changes, and complex human-human and human-object interactions. In this study, we address these challenges by exploring the benefits of a low-level sensor fusion approach that combines grayscale and neuromorphic vision sensor (NVS) data. We first generate a custom dataset using an NVS camera in an indoor environment. We then conduct a comprehensive study by experimenting with different image features and deep learning networks, followed by a multi-input fusion strategy to optimize our experiments with respect to overfitting. Our primary goal is to determine the best input feature types for multi-human motion detection using statistical analysis. We find that there is a significant difference between the input features of optimized backbones, with the best strategy depending on the amount of available data. Specifically, under a low-data regime, event-based frames seem to be the preferred input feature type, while higher data availability benefits the combined use of grayscale and optical flow features. Our results demonstrate the potential of sensor fusion and deep learning techniques for multi-human tracking in indoor surveillance, although it is acknowledged that further studies are needed to confirm our findings.

Fusion Object Detection and Action Recognition to Predict Violent Action.

In the context of Shared Autonomous Vehicles, the need to monitor the environment inside the car will be crucial. This article focuses on the application of deep learning algorithms to present a fusion monitoring solution which was three different algorithms: a violent action detection system, which recognizes violent behaviors between passengers, a violent object detection system, and a lost items detection system. Public datasets were used for object detection algorithms (COCO and TAO) to train state-of-the-art algorithms such as YOLOv5. For violent action detection, the MoLa InCar dataset was used to train on state-of-the-art algorithms such as I3D, R(2+1)D, SlowFast, TSN, and TSM. Finally, an embedded automotive solution was used to demonstrate that both methods are running in real-time.

Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of cranial deformities.

Evaluation of the head shape of newborns is needed to detect cranial deformities, disturbances in head growth, and consequently, to predict short- and long-term neurodevelopment. Currently, there is a lack of automatic tools to provide a detailed evaluation of the head shape. Artificial intelligence (AI) methods, namely deep learning (DL), can be explored to develop fast and automatic approaches for shape evaluation. However, due to the clinical variability of patients' head anatomy, generalization of AI networks to the clinical needs is paramount and extremely challenging. In this work, a new framework is proposed to augment the 3D data used for training DL networks for shape evaluation. The proposed augmentation strategy deforms head surfaces towards different deformities. For that, a point-based 3D morphable model (p3DMM) is developed to generate a statistical model representative of head shapes of different cranial deformities. Afterward, a constrained transformation approach (3DHT) is applied to warp a head surface towards a target deformity by estimating a dense motion field from a sparse one resulted from the p3DMM. Qualitative evaluation showed that the proposed method generates realistic head shapes indistinguishable from the real ones. Moreover, quantitative experiments demonstrated that DL networks training with the proposed augmented surfaces improves their performance in terms of head shape analysis. Overall, the introduced augmentation allows to effectively transform a given head surface towards different deformity shapes, potentiating the development of DL approaches for head shape analysis.

Characterization of the Workspace and Limits of Operation of Laser Treatments for Vascular Lesions of the Lower Limbs.

The increase of the aging population brings numerous challenges to health and aesthetic segments. Here, the use of laser therapy for dermatology is expected to increase since it allows for non-invasive and infection-free treatments. However, existing laser devices require doctors' manually handling and visually inspecting the skin. As such, the treatment outcome is dependent on the user's expertise, which frequently results in ineffective treatments and side effects. This study aims to determine the workspace and limits of operation of laser treatments for vascular lesions of the lower limbs. The results of this study can be used to develop a robotic-guided technology to help address the aforementioned problems. Specifically, workspace and limits of operation were studied in eight vascular laser treatments. For it, an electromagnetic tracking system was used to collect the real-time positioning of the laser during the treatments. The computed average workspace length, height, and width were 0.84 ± 0.15, 0.41 ± 0.06, and 0.78 ± 0.16 m, respectively. This corresponds to an average volume of treatment of 0.277 ± 0.093 m3. The average treatment time was 23.2 ± 10.2 min, with an average laser orientation of 40.6 ± 5.6 degrees. Additionally, the average velocities of 0.124 ± 0.103 m/s and 31.5 + 25.4 deg/s were measured. This knowledge characterizes the vascular laser treatment workspace and limits of operation, which may ease the understanding for future robotic system development.

Interaction of histological events and physiological mediators in healthy placentas from malaria-endemic area in Colombia: An approach with a factorial model.

The biological study of the placenta is fragmented and focused on morbid events. The interaction of histological events and mediators of physiological processes in healthy placentas in malaria-endemic areas is unknown. This study aimed to build a factorial model for the convergence of events and mediators in healthy placentas of women living in northwestern Colombia through a study of 44 placentas. Linear correlations and exploratory factor analysis were carried out with histological events and expression of genes related to mediators. The factor analysis allowed us the identification of three components. The first compound by the following histological variables: number of capillaries and villus, immune cells in villus, atherosis, and CD8+ lymphocytes. The second with articulation of histological variables (syncytyal nodes, fibrinoid deposits, thrombi and immune cells) and physiological mediators of apoptosis and regulation. The thirth included physiological mediators of hypoxia, angiogenesis, pro-inflammation and anti-inflammation. All components presented excellent predictive and construct validity, and excellent goodness of fit parameters. In healthy placentas, the factorial structure of histological events and physiological mediators in three underlying components that support their interactions was demonstrated. These findings are significant because they help improve the study of healthy placental biology in malaria endemic areas and evaluate mechanisms that alter its morphology and function, with their subsequent risk for pregnancy and maternal-fetal health.

AI Based Monitoring of Different Risk Levels in COVID-19 Context.

COVID-19 was responsible for devastating social, economic, and political effects all over the world. Although the health authorities imposed restrictions provided relief and assisted with trying to return society to normal life, it is imperative to monitor people's behavior and risk factors to keep virus transmission levels as low as possible. This article focuses on the application of deep learning algorithms to detect the presence of masks on people in public spaces (using RGB cameras), as well as the detection of the caruncle in the human eye area to make an accurate measurement of body temperature (using thermal cameras). For this task, synthetic data generation techniques were used to create hybrid datasets from public ones to train state-of-the-art algorithms, such as YOLOv5 object detector and a keypoint detector based on Resnet-50. For RGB mask detection, YOLOv5 achieved an average precision of 82.4%. For thermal masks, glasses, and caruncle detection, YOLOv5 and keypoint detector achieved an average precision of 96.65% and 78.7%, respectively. Moreover, RGB and thermal datasets were made publicly available.

Cost-effectiveness of rapid diagnostic tests, compared to microscopic tests, for the diagnosis and treatment of gestational malaria in Colombia from an institutional perspective.

BACKGROUND: Gestational malaria is associated with negative outcomes in maternal and gestational health; timely diagnosis is crucial to avoid complications. However, the limited infrastructure, equipment, test reagents, and trained staff make it difficult to use thick blood smear tests in rural areas, where rapid testing could be a viable alternative. The purpose of this study was to estimate the cost-effectiveness of rapid tests type III (Plasmodium falciparum/Plasmodium spp P.f/pan) versus microscopic tests for the diagnosis and treatment of gestational malaria in Colombia. METHODS: Cost-effectiveness analyses of gestational malaria diagnosis from an institutional perspective using a decision tree. Standard costing was performed for the identification, measurement and assessment phases, with data from Colombian tariff manuals. The data was collected from Health Situation Analysis, SIVIGILA and meta-analysis. Average and incremental cost-effectiveness ratio were estimated. The uncertainty was assessed through probabilistic sensitivity analysis. RESULTS: The cost of rapid diagnostic tests in 3,000 pregnant women with malaria was US$66,936 and 1,182 disability adjusted life years (DALYs) were estimated. The cost using thick blood smear tests was US$50,838 and 1,023 DALYs, for an incremental cost-effectiveness of US$ 101.2. The probabilistic sensitivity analysis of rapid diagnostic tests determined that they are highly cost-effective in 70% of the cases, even below the US$1,200 threshold; also, they showed an incremental net monetary benefit of $150,000 when payer's willingness is US$1,000. CONCLUSION: The use of rapid diagnostic tests for timely diagnosis and treatment of gestational malaria is a highly cost-effective strategy in Colombia, with uncertainty analyses supporting the robustness of this conclusion and the increased net monetary benefit that the health system would obtain. This strategy may help in preventing the negative effects on maternal health and the neonate at a low cost.

[Social determination and determinants of malaria: a systematic review, 1980-2018Determinação social e determinantes sociais da malária: revisão sistemática, 1980-2018]. / Determinación y determinantes sociales de la malaria: revisión sistemática, 1980-2018.

OBJECTIVE: Describe how the 'social determination of health' approach has been applied in malaria studies around the world. METHODS: Systematic review of original studies published from 1980 to 2018. Six search strategies were used in ten multidisciplinary databases, and in libraries and repositories of seven universities in Brazil, Colombia, Ecuador, and Peru. PRISMA guidelines were followed, methodological quality was evaluated according to STROBE criteria, and a qualitative summary of the results was conducted. RESULTS: Ten studies published from 1984 to 2017 met pre-established inclusion and exclusion criteria; 33 social determinants of malaria were identified. Of individual determinants, greater malaria risk was found in adults, people who are often outdoors at night, and people who do not take preventive measures; intermediate determinants were dwellings with poor physical and sanitary infrastructure, overcrowded, located in forested areas, and containing animals. Regarding socioeconomic determinants, the people at greatest risk were involved in agro-forestry activities, migrants, and those with low income and a low educational level. Malaria caused high economic losses and led to poverty and educational delay. CONCLUSION: No studies were found that used Latin American social medicine approaches or that applied the World Health Organization's hierarchical and multilevel analysis for individual, intermediate, and structural determinants. No progress has been made in the analysis of social categories-territory, social class, gender, ethnic group, macroeconomic policies-or other socioeconomic characteristics that determine risk of illness or death from malaria.

OBJETIVO: Descrever como foi aplicado o enfoque da determinação social da saúde nos estudos de malária no mundo. MÉTODOS: Uma revisão sistemática de estudos originais publicados entre 1980 e 2018 foi conduzida com o uso de seis estratégias de busca em 10 bases de dados multidisciplinares e em bibliotecas e repositórios de sete universidades do Brasil, Colômbia, Equador e Peru. O estudo se baseou nos critérios da recomendação PRISMA e a qualidade metodológica foi avaliada segundo os critérios da iniciativa STROBE. Foi realizada uma síntese qualitativa dos resultados. RESULTADOS: Dez estudos publicados entre 1984 e 2017 preencheram os critérios de inclusão e exclusão predefinidos. Foram identificados 33 determinantes sociais da malária. Com relação aos determinantes individuais, foi observado maior risco da malária em adultos, pessoas com hábitos noturnos e que não aderem a práticas preventivas. Os determinantes intermediários foram moradias com infraestruturas física e sanitária precárias, com aglomeração de pessoas, localizadas em áreas de mata e com a presença de animais. E, quanto aos determinantes socioeconômicos, houve maior risco da doença entre os indivíduos que se dedicam a atividades agroflorestais, migrantes e pessoas com baixa escolaridade e baixa renda. A malária causou grande prejuízo econômico, gerando pobreza e atraso educacional. CONCLUSÃO: Não foram encontrados estudos com enfoques da medicina social latino-americana nem estudos contendo análises com modelo hierárquico e multinível para os determinantes individuais, intermediários e estruturais de acordo com a definição da Organização Mundial da Saúde. Não foi possível aprofundar a análise das categorias sociais (território, classe social, gênero, etnia e políticas macroeconômicas) ou outras características socioeconômicas que determinam o risco de ter a doença ou morrer de malária.

Clinical Outcomes of Submicroscopic Infections and Correlates of Protection of VAR2CSA Antibodies in a Longitudinal Study of Pregnant Women in Colombia.

Malaria in pregnancy can cause serious adverse outcomes for the mother and the fetus. However, little is known about the effects of submicroscopic infections (SMIs) in pregnancy, particularly in areas where Plasmodium falciparum and Plasmodium vivax cocirculate. A cohort of 187 pregnant women living in Puerto Libertador in northwest Colombia was followed longitudinally from recruitment to delivery. Malaria was diagnosed by microscopy, reverse transcription-quantitative PCR (RT-qPCR), and placental histopathology. Gestational age, hemoglobin concentration, VAR2CSA-specific IgG levels, and adhesion-blocking antibodies were measured during pregnancy. Statistical analyses were performed to evaluate the impact of SMIs on birth weight and other delivery outcomes. Twenty-five percent of women (45/180) were positive for SMIs during pregnancy. Forty-seven percent of infections (21/45) were caused by P. falciparum, 33% were caused by P. vivax, and 20% were caused by mixed Plasmodium spp. Mixed infections of P. falciparum and P. vivax were associated with lower gestational age at delivery (P = 0.0033), while other outcomes were normal. Over 60% of women had antibodies to VAR2CSA, and there was no difference in antibody levels between those with and without SMIs. The anti-adhesion function of these antibodies was associated with protection from SMI-related anemia at delivery (P = 0.0086). SMIs occur frequently during pregnancy, and while mixed infections of both P. falciparum and P. vivax were not associated with a decrease in birth weight, they were associated with significant risk of preterm birth. We propose that the lack of adverse delivery outcomes is due to functional VAR2CSA antibodies that can protect pregnant women from SMI-related anemia.

Asymptomatic plasmodial infection in pregnant women: A global scenario.

Though asymptomatic plasmodial infection (API) is well known phenomenon and play an important role in different populations and malaria transmission settings, it has received less attention in malaria intervention strategies. This review was aimed to estimate the prevalence of API in pregnant women across the world. The bibliography records relevant to the study were searched on PubMed and Lilacs, till August 15, 2016, without restriction of language. A total of 78 references were identified, of which 29 met the inclusion criteria. The study of the identified reports revealed that the mean prevalence of API in pregnant women was 10.8% (3382/31186), with wide variation among countries and transmission settings. The reports showed that APIs are very common even in low malaria transmission areas, and most of the APIs are due to submicroscopic plasmodial infection (SPI). More sensitive diagnostic tools are required to address API and SPI in such areas. Every malaria endemic region/country should carry out systematic studies for accurate estimation of frequency for both these events (API and SPI) in different populations for planning appropriate intervention measures.

Latent Class Models in action: bridging social capital & Internet usage.

This paper explores how Latent Class Models (LCM) can be applied in social research, when the basic assumptions of regression models cannot be validated. We examine the usefulness of this method with data collected from a study on the relationship between bridging social capital and the Internet. Social capital is defined here as the resources that are potentially available in one's social ties. Bridging is a dimension of social capital, usually related to weak ties (acquaintances), and a source of instrumental resources such as information. The study surveyed a stratified random sample of 417 inhabitants of Lisbon, Portugal. We used LCM to create the variable bridging social capital, but also to estimate the relationship between bridging social capital and Internet usage when we encountered convergence problems with the logistic regression analysis. We conclude by showing a positive relationship between bridging and Internet usage, and by discussing the potential of LCM for social science research.

Submicroscopic infection of placenta by Plasmodium produces Th1/Th2 cytokine imbalance, inflammation and hypoxia in women from north-west Colombia.

BACKGROUND: A large-scale study was set up in order to study the epidemiology, clinical aspects, and immunopathology of gestational and placental malaria in north-west Colombia. In this region, recent reports using a qPCR technique, confirmed frequencies of infection, by Plasmodium falciparum or Plasmodium vivax, up to 45%. Given the high rates of infection observed both in mother and placenta, a first exploratory study was proposed in order to characterize the effect on the inflammation status, tissue damage and hypoxia in Plasmodium spp. infected placentas. METHODS: A descriptive, prospective, cross-sectional design was applied to pregnant women with (PM+) and without (PM-) placental malaria. Messenger RNA expression of Fas, FasL; COX-1, COX-2, HIF, VEGF, and the cytokines IL-2, IL-4, IL-10, IFN-γ and TNF, were measured in peripheral and placental blood using a quantitative PCR. The percentage of apoptotic cells was determined with a TUNEL assay. RESULTS: In total 50 placentas were studied: 25 were positive for submicroscopic infection and 25 were negative for Plasmodium infection. Expression of IL-4 and IL-10 was observed high in placental tissue of PM+, while IL-2 was high in peripheral blood of the same group. Expression of TNF and IFNγ in peripheral blood of the PM + group was high. Similarly, the apoptotic index and Fas expression were significantly high in PM+. However, FasL expression was observed low in PM + compared to PM-. Inflammation markers (HIF, VEGF) and hypoxia markers (COX-1, COX-2) were high in the PM + group. CONCLUSION: During placental malaria expression of some pro-inflammatory cytokines is up-regulated and markers of hypoxia and tissue damage are increased in cases of submicroscopic infection.

Automatic prebent customized prosthesis for pectus excavatum minimally invasive surgery correction.

Pectus excavatum is the most common deformity of the thorax. A minimally invasive surgical correction is commonly carried out to remodel the anterior chest wall by using an intrathoracic convex prosthesis in the substernal position. The process of prosthesis modeling and bending still remains an area of improvement. The authors developed a new system, i3DExcavatum, which can automatically model and bend the bar preoperatively based on a thoracic CT scan. This article presents a comparison between automatic and manual bending. The i3DExcavatum was used to personalize prostheses for 41 patients who underwent pectus excavatum surgical correction between 2007 and 2012. Regarding the anatomical variations, the soft-tissue thicknesses external to the ribs show that both symmetric and asymmetric patients always have asymmetric variations, by comparing the patients' sides. It highlighted that the prosthesis bar should be modeled according to each patient's rib positions and dimensions. The average differences between the skin and costal line curvature lengths were 84 ± 4 mm and 96 ± 11 mm, for male and female patients, respectively. On the other hand, the i3DExcavatum ensured a smooth curvature of the surgical prosthesis and was capable of predicting and simulating a virtual shape and size of the bar for asymmetric and symmetric patients. In conclusion, the i3DExcavatum allows preoperative personalization according to the thoracic morphology of each patient. It reduces surgery time and minimizes the margin error introduced by the manually bent bar, which only uses a template that copies the chest wall curvature.

Automatic multi-view pose estimation in focused cardiac ultrasound.

Focused cardiac ultrasound (FoCUS) is a valuable point-of-care method for evaluating cardiovascular structures and function, but its scope is limited by equipment and operator's experience, resulting in primarily qualitative 2D exams. This study presents a novel framework to automatically estimate the 3D spatial relationship between standard FoCUS views. The proposed framework uses a multi-view U-Net-like fully convolutional neural network to regress line-based heatmaps representing the most likely areas of intersection between input images. The lines that best fit the regressed heatmaps are then extracted, and a system of nonlinear equations based on the intersection between view triplets is created and solved to determine the relative 3D pose between all input images. The feasibility and accuracy of the proposed pipeline were validated using a novel realistic in silico FoCUS dataset, demonstrating promising results. Interestingly, as shown in preliminary experiments, the estimation of the 2D images' relative poses enables the application of 3D image analysis methods and paves the way for 3D quantitative assessments in FoCUS examinations.

Deep-DM: Deep-driven deformable model for 3D image segmentation using limited data.

Objective - Medical image segmentation is essential for several clinical tasks, including diagnosis, surgical and treatment planning, and image-guided interventions. Deep Learning (DL) methods have become the state-of-the-art for several image segmentation scenarios. However, a large and well-annotated dataset is required to effectively train a DL model, which is usually difficult to obtain in clinical practice, especially for 3D images. Methods - In this paper, we proposed Deep-DM, a learning-guided deformable model framework for 3D medical imaging segmentation using limited training data. In the proposed method, an energy function is learned by a Convolutional Neural Network (CNN) and integrated into an explicit deformable model to drive the evolution of an initial surface towards the object to segment. Specifically, the learning-based energy function is iteratively retrieved from localized anatomical representations of the image containing the image information around the evolving surface at each iteration. By focusing on localized regions of interest, this representation excludes irrelevant image information, facilitating the learning process. Results and conclusion - The performance of the proposed method is demonstrated for the tasks of left ventricle and fetal head segmentation in ultrasound, left atrium segmentation in Magnetic Resonance, and bladder segmentation in Computed Tomography, using different numbers of training volumes in each study. The results obtained showed the feasibility of the proposed method to segment different anatomical structures in different imaging modalities. Moreover, the results also showed that the proposed approach is less dependent on the size of the training dataset in comparison with state-of-the-art DL-based segmentation methods, outperforming them for all tasks when a low number of samples is available. Significance - Overall, by offering a more robust and less data-intensive approach to accurately segmenting anatomical structures, the proposed method has the potential to enhance clinical tasks that require image segmentation strategies.