Deep Cascade Networks for Single 2D US Slice to 3D CT/MRI Image Registration

Background and Objective: Ultrasound (US) devices are often used in percutanous interventions. Due to their low image quality, the US image slices are aligned with pre-operative Computed Tomography/Magnetic Resonance Imaging (CT/MRI) images to enable better visibilities of anatomies during the intervention. This work aims at improving the deep learning one shot registration by using less loops through deep learning networks.Methods: We propose two cascade networks which aim at improving registration accuracy by less loops. The InitNet-Regression-LoopNet (IRL) network applies the plane regression method to detect the orientation of the predicted plane derived from the previous loop, then corrects input CT/MRI volume orientation and improves the prediction iteratively. The InitNet-LoopNet-MultiChannel (ILM) comprises two cascade networks, where an InitNet is trained with low resolution images toperform coarse registration. Then, a LoopNet wraps the high resolution images and result of the previous loop into a three channel input and trained to improve prediction accuracy in every loop. Results: We benchmark the two cascade networks on 1035 clinical images from 52 patients , yielding an improved registration accuracy with LoopNet. The IRL achieved an average angle error of 13.3° and an average distance error of 4.5 millimieter. It out-performs the ILM network with angle error 17.4° and distance error 4.9 millimeter and the InitNet with angle error 18.6° and distance error 4.9 millimeter. Our results show the efficiency of the proposed registration networks, which have the potential to improve the robustness and accuracy of intraoperative patient registration.

Communication energy optimization of electric vehicle platoon on curved road

The cruising range of an electric vehicle is limited by its battery. Reducing the energy consumption of MES (main energy systems) or AES (auxiliary energy systems) of the vehicle battery is an effective means to increase the electric vehicle cruising range. Platoon driving can greatly reduce the wind resistance of the vehicle and then reduce the energy consumption of MES for electric vehicles. This paper proposes an adaptive communication energy optimization scheme based on road curvature radius to save the energy of AES for the electric vehicle platoon on curved roads. In this paper, the inter-vehicle distance error based on the car-like model in a two-dimensional space is established. Then, the inter-vehicle distance error is used to design a control law K to accomplish successful platooning. Next, three platooning control schemes based on different information flow topologies are discussed. Finally, the consensus of three platooning control schemes and the energy consumption of electric vehicle communication systems are analyzed by MATLAB’s Simulink. Simulation results show that the communication energy optimization scheme reduces the power consumption of AES as long as the platoon driving on curved roads.

Study of the Error Caused by Camera Movement for the Stereo-Vision System

The stereo-vision system plays an increasingly important role in various fields of research and applications. However, inevitable slight movements of cameras under harsh working conditions can significantly influence the 3D measurement accuracy. This paper focuses on the effect of camera movements on the stereo-vision 3D measurement. The camera movements are divided into four categories, viz., identical translations and rotations, relative translation and rotation. The error models of 3D coordinate and distance measurement are established. Experiments were performed to validate the mathematical models. The results show that the 3D coordinate error caused by identical translations increases linearly with the change in the positions of both cameras, but the distance measurement is not affected. For identical rotations, the 3D coordinate error introduced only in the rotating plane is proportional to the rotation angle within 10° while the distance error is zero. For relative translation, both coordinate and distance errors keep linearly increasing with the change in the relative positions. For relative rotation, the relationship between 3D coordinate error and rotation angle can be described as the nonlinear trend similar to a sine-cosine curve. The impact of the relative rotation angle on distance measurement accuracy does not increase monotonically. The relative rotation is the main factor compared to other cases. Even for the occurrence of a rotation angle of 10°, the resultant maximum coordinate error is up to 2000 mm, and the distance error reaches 220%. The results presented are recommended as practice guidelines to reduce the measurement errors.

PSXVI-20 Real-time localization system for dairy cattle in a commercial facility: Evaluation of dynamic positioning ability

Real-time location tracking of dairy cattle on commercial facilities enables producers to quickly locate and tend to cows and provides insight into health status and animal welfare. The effectiveness of these systems depends on accurate and reliable reporting of information. Therefore, this experiment’s objective was to assess the dynamic positioning abilities of a commercially available real-time location ear tag system (SmartBow; Zoetis, Parsippany, NJ) for use in group-housed dairy cattle. Using 2 pens of a freestall barn, cameras (n = 18) were installed on barn fixtures and positioned to photograph cow usage of feedbunk (n = 4), stall (n = 10), or waterer (n = 4). Photographs were captured automatically at 1-min intervals for 85 hours, consecutively. Laser measurements determined X and Y coordinates for reference point locations respective to camera. In dynamic position assessments, variations in ear tag proximity to measured reference points are to be expected. One trained observer reviewed photographs, saving only those depicting cows with visible farm-assigned ID. Data points were only defined if farm-assigned ID was associated with an active technology ear tag. For data analyses, technology-reported X and Y coordinates of data points were compared to reference point locations. Of all data points (n = 114,633), mean distance error was 3.82 m (SD= 6.73 m). Median distance error was 2.44 m (SD= 6.73; Q1= 1.41 m; Q3=5.13 m). With removal of outliers (99th percentile), data (n = 113,487) had a mean distance error of 3.82 m (min= 0.01 m; max= 33.80 m) and a median of 2.41 m (SD= 2.78 m; Q1= 1.40 m; Q3= 4.95 m). Using distance error, mean percentages of data points that fell within specified radii of circles centered about reference point locations were calculated (Table 1). These results suggest that this ear tag technology can accurately report the dynamic positions of dairy cattle moving freely in a commercial freestall barn environment.

Communication Energy Optimization of Electric Vehicle Platoon on Curved Road

The cruising range of an electric vehicle is limited by its battery. Reducing the energy consumption of MES (main energy systems) or AES (auxiliary energy systems) of the vehicle battery is an effective means to increase the electric vehicle cruising range. Platoon driving can greatly reduce the wind resistance of the vehicle and then reduce the energy consumption of MES for electric vehicles. This paper proposes an adaptive communication energy optimization scheme based on road curvature radius to save the energy of AES for the electric vehicle platoon on curved roads. In this paper, the inter-vehicle distance error based on the car-like model in a two-dimensional space is established. Then, the inter-vehicle distance error is used to design a control law K to accomplish successful platooning. Next, three platooning control schemes based on different information flow topologies are discussed. Finally, the consensus of three platooning control schemes and the energy consumption of electric vehicle communication systems are analyzed by MATLAB's Simulink. Simulation results show that the communication energy optimization scheme reduces the power consumption of AES as long as the platoon driving on curved roads.

Validation of Automated Ultrasound-based Registration for Navigated Scaphoid Fixation

Fractures of the scaphoid bone may be treated in a minimally-invasive fashion. Conventionally, fluoroscopy is required to guide the placement of an osteosynthesis screw. In this work, an alternative method based on volumetric ultrasound is validated. Methods: The fully automatic and fast image processing pipeline involves two machine learning architectures for segmentation and registration. A pre-operatively acquired 3D bone model is registered to the 3D bone surface segmented from the intra-operative ultrasound. Screw positioning is planned in an automated fashion and evaluated in an in-vitro setting: Volumetric ultrasound images of a 3D-printed phantom of a human wrist are acquired for 22 different probe poses. For 220 test runs with different initial displacements, the resulting screw placement within a defined safe zone is evaluated. If the screw lies within the safe zone, its placement is assumed to be successful. Results: An isolated analysis of the registration results in a surface distance error of the registered meshes of 0.49 ± 0.01mm, with successful screw placement in all of the evaluated 220 test runs. The full pipeline, combining segmentation and registration, achieves a mean surface distance error of 0.79 ± 0.37mm, leading to successful screw placements for 149 out of 220 test runs. Poses not suited for the registration could be determined. Excluding these from the analysis, 139 out of 160 test runs are successful. Conclusion: The method proves to be promising when evaluating the registration alone, even given the challenging setup of sub-optimal probe positions. The experiments also demonstrate that further improvement regarding the segmentation is necessary.