Robust Kernel-based Feature Representation for 3D Point Cloud Analysis via Circular Graph Convolutional Network

Feature descriptors of point clouds are used in several applications, such as registration and part segmentation of 3D point clouds. Learning discriminative representations of local geometric features is unquestionably the most important task for accurate point cloud analyses. However, it is challenging to develop rotation or scale-invariant descriptors. Most previous studies have either ignored rotations or empirically studied optimal scale parameters, which hinders the applicability of the methods for real-world datasets. In this paper, we present a new local feature description method that is robust to rotation, density, and scale variations. Moreover, to improve representations of the local descriptors, we propose a global aggregation method. First, we place kernels aligned around each point in the normal direction. To avoid the sign problem of the normal vector, we use a symmetric kernel point distribution in the tangential plane. From each kernel point, we first projected the points from the spatial space to the feature space, which is robust to multiple scales and rotation, based on angles and distances. Subsequently, we perform graph convolutions by considering local kernel point structures and long-range global context, obtained by a global aggregation method. We experimented with our proposed descriptors on benchmark datasets (i.e., ModelNet40 and ShapeNetPart) to evaluate the performance of registration, classification, and part segmentation on 3D point clouds. Our method showed superior performances when compared to the state-of-the-art methods by reducing 70$\%$ of the rotation and translation errors in the registration task. Our method also showed comparable performance in the classification and part-segmentation tasks with simple and low-dimensional architectures.Comment: 10 pages, 9 figure

Tooth Instance Segmentation from Cone-Beam CT Images through Point-based Detection and Gaussian Disentanglement

Individual tooth segmentation and identification from cone-beam computed tomography images are preoperative prerequisites for orthodontic treatments. Instance segmentation methods using convolutional neural networks have demonstrated ground-breaking results on individual tooth segmentation tasks, and are used in various medical imaging applications. While point-based detection networks achieve superior results on dental images, it is still a challenging task to distinguish adjacent teeth because of their similar topologies and proximate nature. In this study, we propose a point-based tooth localization network that effectively disentangles each individual tooth based on a Gaussian disentanglement objective function. The proposed network first performs heatmap regression accompanied by box regression for all the anatomical teeth. A novel Gaussian disentanglement penalty is employed by minimizing the sum of the pixel-wise multiplication of the heatmaps for all adjacent teeth pairs. Subsequently, individual tooth segmentation is performed by converting a pixel-wise labeling task to a distance map regression task to minimize false positives in adjacent regions of the teeth. Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art approaches by increasing the average precision of detection by 9.1%, which results in a high performance in terms of individual tooth segmentation. The primary significance of the proposed method is two-fold: 1) the introduction of a point-based tooth detection framework that does not require additional classification and 2) the design of a novel loss function that effectively separates Gaussian distributions based on heatmap responses in the point-based detection framework.Comment: 11 pages, 7 figure

Improving 3D Imaging with Pre-Trained Perpendicular 2D Diffusion Models

Diffusion models have become a popular approach for image generation and reconstruction due to their numerous advantages. However, most diffusion-based inverse problem-solving methods only deal with 2D images, and even recently published 3D methods do not fully exploit the 3D distribution prior. To address this, we propose a novel approach using two perpendicular pre-trained 2D diffusion models to solve the 3D inverse problem. By modeling the 3D data distribution as a product of 2D distributions sliced in different directions, our method effectively addresses the curse of dimensionality. Our experimental results demonstrate that our method is highly effective for 3D medical image reconstruction tasks, including MRI Z-axis super-resolution, compressed sensing MRI, and sparse-view CT. Our method can generate high-quality voxel volumes suitable for medical applications.Comment: ICCV23 poster. 15 pages, 9 figure

Empagliflozin Contributes to Polyuria via Regulation of Sodium Transporters and Water Channels in Diabetic Rat Kidneys

Besides lowering glucose, empagliflozin, a selective sodium-glucose cotransporter-2 (SGLT2) inhibitor, have been known to provide cardiovascular and renal protection due to effects on diuresis and natriuresis. However, the natriuretic effect of SGLT2 inhibitors has been reported to be transient, and long-term data related to diuretic change are sparse. This study was performed to assess the renal effects of a 12-week treatment with empagliflozin (3 mg/kg) in diabetic OLETF rats by comparing it with other antihyperglycemic agents including lixisenatide (10 μg/kg), a glucagon-like peptide receptor-1 agonist, and voglibose (0.6 mg/kg), an α-glucosidase inhibitor. At 12 weeks of treatment, empagliflozin-treated diabetic rats produced still high urine volume and glycosuria, and showed significantly higher electrolyte-free water clearance than lixisenatide or voglibose-treated diabetic rats without significant change of serum sodium level and fractional excretion of sodium. In empagliflozin-treated rats, renal expression of Na+-Cl- cotransporter was unaltered, and expressions of Na+/H+ exchanger isoform 3, Na+-K+-2Cl- cotransporter, and epithelial Na+ channel were decreased compared with control diabetic rats. Empagliflozin increased an expression of aquaporin (AQP)7 but did not affect AQP3 and AQP1 protein expressions in diabetic kidneys. Despite the increased expression in vasopressin V2 receptor, protein and mRNA levels of AQP2 in empagliflozin-treated diabetic kidneys were significantly decreased compared to control diabetic kidneys. In addition, empagliflozin resulted in the increased phosphorylation of AQP2 at S261 through the increased cyclin-dependent kinases 1 and 5 and protein phosphatase 2B. These results suggest that empagliflozin may contribute in part to polyuria via its regulation of sodium channels and AQP2 in diabetic kidneys