Research on improved YOLOv5 femur segmentation and point cloud reconstruction algorithm

Traditional bone and meat segmentation relies on operators' experience and manual operation, resulting in low efficiency and high cost. Introducing three-dimensional information to assist in bone and meat segmentation can help improve the efficiency of bone removal work and save costs. The three-dimensional information comes from the bone contours in the X-ray image sequence. In this paper, an improved YOLOv5 algorithm and point cloud reconstruction method are used to achieve accurate segmentation of the femoral contour. The improved algorithm incorporates Conv convolution and CA coordinate attention mechanism in the neck, enhancing the local feature extraction ability of the algorithm; At the same time, C3 was replaced with SENetV2 module in the backbone, which improved the precision of feature expression. Using motion recovery structure algorithm to reconstruct sparse point clouds from segmented contours. Compared to the original model, the segmentation accuracy [email protected] and [email protected] ~0.95 has been improved, laying a good foundation for subsequent point cloud processing and cutting path planning