Early Term Effects of rhBMP-2 on Pedicle Screw Fixation in a Sheep Model: Histomorphometric and Biomechanical Analyses

Background: The effects of recombinant human bone morphogenetic protein-2 (rhBMP-2) on pedicle screw pullout force and its potential to improve spinal fixation have not previously been investigated. rhBMP-2 on an absorbable collagen sponge (ACS) carrier was delivered in and around cannulated and fenestrated pedicle screws in a sheep lumbar spine instability model. Two control groups (empty screw and ACS with buffer) were also evaluated. We hypothesized that rhBMP-2 could stimulate bone growth in and around the cannulated and fenestrated pedicle screws to improve early bone purchase. Methods: Eight skeletally mature sheep underwent destabilizing laminectomies at L2–L3 and L4–L5 followed by stabilization with pedicle screw and rod constructs. An ACS carrier was used to deliver 0.15 mg of rhBMP-2 within and around the cannulated and fenestrated titanium pedicle screws. Biomechanics and histomorphometry were used to evaluate the early term results at 6 and 12 postoperative weeks. Results: rhBMP-2 was unable to improve bony purchase of the cannulated and fenestrated pedicle screws compared to both control groups. Although rhBMP-2 groups had pullout forces that were less than both control groups, both rhBMP-2 groups had pullout force values exceeding 2,000 N, which was comparable to previously published results for unmodified pedicle screws. Significant differences in the percentages of bone in peri-screw tissues was not observed amongst the four treatment groups. Microradiography and quantitative histomorphometry showed that at 6 weeks, rhBMP-2 induced peri-screw remodeling regions containing peri-implant bone which was hypodense with respect to surrounding native trabeculae. A moderate correlation between biomechanical pullout variables and histomorphometry data was observed. Conclusions: The design of the cannulated and fenestrated pedicle screw was able to facilitate new bone formation to achieve high pullout forces. However, delivery of rhBMP-2 should be carefully controlled to prevent excessive bone remodeling which could cause early screw loosening

Depthformer : Multiscale Vision Transformer For Monocular Depth Estimation With Local Global Information Fusion

Attention-based models such as transformers have shown outstanding performance on dense prediction tasks, such as semantic segmentation, owing to their capability of capturing long-range dependency in an image. However, the benefit of transformers for monocular depth prediction has seldom been explored so far. This paper benchmarks various transformer-based models for the depth estimation task on an indoor NYUV2 dataset and an outdoor KITTI dataset. We propose a novel attention-based architecture, Depthformer for monocular depth estimation that uses multi-head self-attention to produce the multiscale feature maps, which are effectively combined by our proposed decoder network. We also propose a Transbins module that divides the depth range into bins whose center value is estimated adaptively per image. The final depth estimated is a linear combination of bin centers for each pixel. Transbins module takes advantage of the global receptive field using the transformer module in the encoding stage. Experimental results on NYUV2 and KITTI depth estimation benchmark demonstrate that our proposed method improves the state-of-the-art by 3.3%, and 3.3% respectively in terms of Root Mean Squared Error (RMSE). Code is available at https://github.com/ashutosh1807/Depthformer.git

Intent Preserving 360 Video Stabilization Using Constrained Optimization

A system and method are disclosed, that solve for rotational updates in 360 videos by removing camera shakes, while preserving user intended motions. The method uses a constrained nonlinear optimization approach in quaternion space. At first, optimal 3D camera rotations are computed between key frames. 3D camera rotations between consecutive frames are then computed. The first, second, and third derivatives of the resulting camera path are minimized, to stabilize the camera orientation path. The computation strives to find a smooth path, while also limiting its deviation from the original path. The system keeps the orientations close to the original, for example, even when the videographer takes a turn. Each frame is then warped to the stabilized path, which results in a smoother video. The rotational camera updates may be applied to the input stream at source or added as metadata. The technology may influence standards by making rotational updates metadata a component of 360 videos. KEYWORDS: 360 degree video, camera rotation, removing camera shake, computing camera rotatio

A Joint 3D-2D based Method for Free Space Detection on Roads

In this paper, we address the problem of road segmentation and free space detection in the context of autonomous driving. Traditional methods either use 3-dimensional (3D) cues such as point clouds obtained from LIDAR, RADAR or stereo cameras or 2-dimensional (2D) cues such as lane markings, road boundaries and object detection. Typical 3D point clouds do not have enough resolution to detect fine differences in heights such as between road and pavement. Image based 2D cues fail when encountering uneven road textures such as due to shadows, potholes, lane markings or road restoration. We propose a novel free road space detection technique combining both 2D and 3D cues. In particular, we use CNN based road segmentation from 2D images and plane/box fitting on sparse depth data obtained from SLAM as priors to formulate an energy minimization using conditional random field (CRF), for road pixels classification. While the CNN learns the road texture and is unaffected by depth boundaries, the 3D information helps in overcoming texture based classification failures. Finally, we use the obtained road segmentation with the 3D depth data from monocular SLAM to detect the free space for the navigation purposes. Our experiments on KITTI odometry dataset, Camvid dataset, as well as videos captured by us, validate the superiority of the proposed approach over the state of the art.Comment: Accepted for publication at IEEE WACV 201