Orientation-guided Graph Convolutional Network for Bone Surface Segmentation

Autor:	Rahman, Aimon, Bandara, Wele Gedara Chaminda, Valanarasu, Jeya Maria Jose, Hacihaliloglu, Ilker, Patel, Vishal M
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	Due to imaging artifacts and low signal-to-noise ratio in ultrasound images, automatic bone surface segmentation networks often produce fragmented predictions that can hinder the success of ultrasound-guided computer-assisted surgical procedures. Existing pixel-wise predictions often fail to capture the accurate topology of bone tissues due to a lack of supervision to enforce connectivity. In this work, we propose an orientation-guided graph convolutional network to improve connectivity while segmenting the bone surface. We also propose an additional supervision on the orientation of the bone surface to further impose connectivity. We validated our approach on 1042 vivo US scans of femur, knee, spine, and distal radius. Our approach improves over the state-of-the-art methods by 5.01% in connectivity metric. Comment: Accepted at MICCAI 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2206.08481 Zobrazit plný text záznamu View this record from Arxiv