Producción CyT
Proceedings of the 2024 IEEE International Symposium on Biomedical Imaging (ISBI), 1-4 - Fitting Skeletal Models via Graph-Based Learning
Congreso
Fecha:
2024Editorial y Lugar de Edición:
IEEEResumen *
Skeletonization is a popular shape analysis technique that models an object’s interior as opposed to just its boundary. Fitting template-based skeletal models is a time-consuming process requiring much manual parameter tuning. Recently, machine learning-based methods have shown promise for generating s-reps from object boundaries. In this work, we propose a new skeletonization method which leverages graph convolutional networks to produce skeletal representations (s-reps) from dense segmentation masks. The method is evaluated on both synthetic data and real hippocampus segmentations, achieving promising results and fast inference. Información suministrada por el agente en SIGEVAPalabras Clave
machine learningcomputer graphicsmedical image segmentationgraphs