3 documents found, page 1 of 1
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,...
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...
A review of image processing methods for fetal head and brain analysis in ultra...
Torres, Helena R.; Morais, Pedro André Gonçalves; Oliveira, Bruno; Birdir, Cahit; Rudiger, Mario; Fonseca, Jaime C.; Vilaça, João L.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 liter...