3 documents found, page 1 of 1
Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of...
Torres, Helena R.; Oliveira, Bruno; Morais, Pedro André Gonçalves; Fritze, Anne; Rüdiger, Mario; Fonseca, Jaime C.; Vilaça, João L.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 evalu...
Fetal head circumference delineation using convolutional neural networks with r...
Torres, Helena R.; Oliveira, Bruno; Morais, Pedro; Fritze, Anne; Birdir, Cahit; Rüdiger, Mario; Fonseca, Jaime C.; Vilaça, João L.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...
Anthropometric landmarking for diagnosis of cranial deformities: validation of ...
Torres, Helena Daniela Ribeiro; Morais, Pedro; Fritze, Anne; Burkhardt, Wolfram; Kaufmann, Maxi; Oliveira, Bruno; Veloso, Fernando; Hahn, GabrieleShape analysis of infant’s heads is crucial to diagnose cranial deformities and evaluate head growth. Currently available 3D imaging systems can be used to create 3D head models, promoting the clinical practice for head evaluation. However, manual analysis of 3D shapes is difficult and operator-dependent, causing inaccuracies in the analysis. This study aims to validate an automatic landmark detection method fo...