8 documents found, page 1 of 1
Infant head and brain segmentation from magnetic resonance images using fusion-...
Torres, Helena; Oliveira, Bruno; Morais, Pedro; Fritze, Anne; Hahn, Gabriele; Rudiger, Mario; Fonseca, Jaime; Vilaça, JoãoMagnetic resonance (MR) imaging is widely used for assessing infant head and brain development and for diagnosing pathologies. The main goal of this work is the development of a segmentation framework to create patient-specific head and brain anatomical models from MR images for clinical evaluation. The proposed strategy consists of a fusion-based Deep Learning (DL) approach that combines the information of dif...
Deep-DM: Deep-driven deformable model for 3D image segmentation using limited data
Torres, Helena; Oliveira, Bruno; Fritze, Anne; Birdir, Cahit; Rudiger, Mario; Fonseca, Jaime; Morais, Pedro; Vilaça, JoãoObective - 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,...
Realistic 3D infant head surfaces augmentation to improve AI-based diagnosis of...
Torres, Helena; Oliveira, Bruno; Morais, Pedro; Fritze, Anne; Rudiger, Mario; Fonseca, Jaime C.; Vilaça, João L.Shape 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...
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...
3D facial landmark localization for cephalometric analysis
Torres, Helena R.; Morais, Pedro André Gonçalves; Fritze, Anne; Oliveira, Bruno; Veloso, Fernando; Rudiger, Mario; Fonseca, Jaime C.; Vilaça, João L.Cephalometric analysis is an important and routine task in the medical field to assess craniofacial development and to diagnose cranial deformities and midline facial abnormalities. The advance of 3D digital techniques potentiated the development of 3D cephalometry, which includes the localization of cephalometric landmarks in the 3D models. However, manual labeling is still applied, being a tedious and time-co...
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...
Anthropometric landmark detection in 3D head surfaces using a deep learning app...
Torres, Helena R.; Morais, Pedro André Gonçalves; Fritze, Anne; Oliveira, Bruno; Veloso, Fernando; Rudiger, Mario; Fonseca, Jaime C.; Vilaça, João L.Landmark labeling in 3D head surfaces is an important and routine task in clinical practice to evaluate head shape, namely to analyze cranial deformities or growth evolution. However, manual labeling is still applied, being a tedious and time-consuming task, highly prone to intra-/inter-observer variability, and can mislead the diagnose. Thus, automatic methods for anthropometric landmark detection in 3D models...