HybridPoint: Point Cloud Registration Based on Hybrid Point Sampling and Matching

Patch-to-point matching has become a robust way of point cloud registration. However, previous patch-matching methods employ superpoints with poor localization precision as nodes, which may lead to ambiguous patch partitions. In this paper, we propose a HybridPoint-based network to find more robust and accurate correspondences. Firstly, we propose to use salient points with prominent local features as nodes to increase patch repeatability, and introduce some uniformly distributed points to complete the point cloud, thus constituting hybrid points. Hybrid points not only have better localization precision but also give a complete picture of the whole point cloud. Furthermore, based on the characteristic of hybrid points, we propose a dual-classes patch matching module, which leverages the matching results of salient points and filters the matching noise of non-salient points. Experiments show that our model achieves state-of-the-art performance on 3DMatch, 3DLoMatch, and KITTI odometry, especially with 93.0% Registration Recall on the 3DMatch dataset. Our code and models are available at https://github.com/liyih/HybridPoint.Comment: Accepted by IEEE International Conference on Multimedia and Expo (ICME), 202

The effectiveness of ultrasound-guided core needle biopsy in detecting lymph node metastases in the axilla in patients with breast cancer: systematic review and meta-analysis

Objective: This study aimed to perform a meta-analysis to investigate the diagnostic safety and accuracy of Ultrasound-Guided Core Needle Biopsy (US-CNB) Axillary Lymph Nodes (ALNs) region in patients with Breast Cancer (BC). Methods: The authors searched the electronic databases PubMed, Scopus, Embase, and Web of Science for clinical trials about US-CNB for the detection of ALNs in breast cancer patients. The authors extracted and pooled raw data from the included studies and performed statistical analyses using Meta-DiSc 1.4 and Review Manager 5.3 software. A random effects model was used to calculate the data. At the same time, data from the Ultrasound-guided Fine-Needle Aspiration (US-FNA) were introduced for comparison with the US-CNB. In addition, the subgroup was performed to explore the causes of heterogeneity. (PROSPERO ID: CRD42022369491). Results: In total, 18 articles with 2521 patients were assessed as meeting the study criteria. The overall sensitivity was 0.90 (95% CI [Confidence Interval], 0.87‒0.91; p = 0.00), the overall specificity was 0.99 (95% CI 0.98‒1.00; p = 0.62), the overall area under the curve (AUC) was 0.98. Next, in the comparison of US-CNB and US-FNA, US-CNB is better than US-FNA in the diagnosis of ALNs metastases. The sensitivity was 0.88 (95% CI 0.84‒0.91; p = 0.12) vs. 0.73 (95% CI 0.69‒0.76; p = 0.91), the specificity was 1.00 (95% CI 0.99‒1.00; p = 1.00) vs. 0.99 (95% CI 0.67‒0.74; p = 0.92), and the AUC was 0.99 vs. 0.98. Subgroup analysis showed that heterogeneity may be related to preoperative Neoadjuvant Chemotherapy (NAC) treatment, region, size of tumor diameter, and the number of punctures. Conclusion: US-CNB has a satisfactory diagnostic performance with good specificity and sensitivity in the preoperative diagnosis of ALNs in BC patients