Duong Q. Nguyen1, Thinh D. Le3, Phuong D. Nguyen3, Nga T. K. Le2, H. Nguyen-Xuan3,*
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2197-2214, 2024, DOI:10.32604/cmes.2023.043992
Abstract Facial wound segmentation plays a crucial role in preoperative planning and optimizing patient outcomes in various medical applications. In this paper, we propose an efficient approach for automating 3D facial wound segmentation using a two-stream graph convolutional network. Our method leverages the Cir3D-FaIR dataset and addresses the challenge of data imbalance through extensive experimentation with different loss functions. To achieve accurate segmentation, we conducted thorough experiments and selected a high-performing model from the trained models. The selected model demonstrates exceptional segmentation performance for complex 3D facial wounds. Furthermore, based on the segmentation model, we propose an improved approach for extracting… More >
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