A Multi-Institution Deformable Registration Accuracy Study2

The unmanned aerial vehicle (UAV)-mounted mobile LiDAR system (ULS) is widely used for geomatics owing to its efficient data acquisition and convenient operation. However, due to limited carrying capacity of a UAV, sensors integrated in the ULS should be small and lightweight, which results in decrease in the density of the collected scanning points. This affects registration between image data and point cloud data. To address this issue, the authors propose a method for registering and fusing ULS sequence images and laser point clouds, wherein they convert the problem of registering point cloud data and image data into a problem of matching feature points between the two images. First, a point cloud is selected to produce an intensity image. Subsequently, the corresponding feature points of the intensity image and the optical image are matched, and exterior orientation parameters are solved using a collinear equation based on image position and orientation. Finally, the sequence images are fused with the laser point cloud, based on the Global Navigation Satellite System (GNSS) time index of the optical image, to generate a true color point cloud. The experimental results show the higher registration accuracy and fusion speed of the proposed method, thereby demonstrating its accuracy and effectiveness.

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Automated Emission Data Registration and Segmentation for IC Analysis

ISTFA 2016: Conference Proceedings from the 42nd International Symposium for Testing and Failure Analysis ◽

10.31399/asm.cp.istfa2016p0506 ◽

2016 ◽

Author(s):

Franco Stellari ◽

Peilin Song

Keyword(s):

Data Analysis ◽

Registration Accuracy ◽

Emission Data ◽

Data Registration ◽

Different Types ◽

Test Chips

Abstract In this paper, the development of advanced emission data analysis methodologies for IC debugging and characterization is discussed. Techniques for automated layout to emission registration and data segmentations are proposed and demonstrated using both 22 nm and 14 nm SOI test chips. In particular, gate level registration accuracy is leveraged to compare the emission of different types of gates and quickly create variability maps automatically.

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Laparoscopic augmented reality registration for oncological resection site repair

International Journal of Computer Assisted Radiology and Surgery ◽

10.1007/s11548-021-02336-x ◽

2021 ◽

Author(s):

Fabian Joeres ◽

Tonia Mielke ◽

Christian Hansen

Keyword(s):

Augmented Reality ◽

Time Pressure ◽

Tumour Resection ◽

Anatomical Landmarks ◽

Proof Of Concept ◽

Registration Accuracy ◽

Target Registration Error ◽

Concept Evaluation ◽

Navigation Support ◽

Challenges And Opportunities

Abstract Purpose Resection site repair during laparoscopic oncological surgery (e.g. laparoscopic partial nephrectomy) poses some unique challenges and opportunities for augmented reality (AR) navigation support. This work introduces an AR registration workflow that addresses the time pressure that is present during resection site repair. Methods We propose a two-step registration process: the AR content is registered as accurately as possible prior to the tumour resection (the primary registration). This accurate registration is used to apply artificial fiducials to the physical organ and the virtual model. After the resection, these fiducials can be used for rapid re-registration (the secondary registration). We tested this pipeline in a simulated-use study with $$N=18$$ N = 18 participants. We compared the registration accuracy and speed for our method and for landmark-based registration as a reference. Results Acquisition of and, thereby, registration with the artificial fiducials were significantly faster than the initial use of anatomical landmarks. Our method also had a trend to be more accurate in cases in which the primary registration was successful. The accuracy loss between the elaborate primary registration and the rapid secondary registration could be quantified with a mean target registration error increase of 2.35 mm. Conclusion This work introduces a registration pipeline for AR navigation support during laparoscopic resection site repair and provides a successful proof-of-concept evaluation thereof. Our results indicate that the concept is better suited than landmark-based registration during this phase, but further work is required to demonstrate clinical suitability and applicability.

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Quantitative Assessment of Shape Deformation of Regional Cranial Bone for Evaluation of Surgical Effect in Patients with Craniosynostosis

Applied Sciences ◽

10.3390/app11030990 ◽

2021 ◽

Vol 11 (3) ◽

pp. 990

Author(s):

Min Jin Lee ◽

Helen Hong ◽

Kyu Won Shim

Keyword(s):

Quantitative Assessment ◽

Deformable Registration ◽

Occipital Bone ◽

Cranial Bone ◽

Shape Deformation ◽

Surface Model ◽

Cephalic Index ◽

Skull Shape ◽

Quantification Method ◽

Parietal Bones

Surgery in patients with craniosynostosis is a common treatment to correct the deformed skull shape, and it is necessary to verify the surgical effect of correction on the regional cranial bone. We propose a quantification method for evaluating surgical effects on regional cranial bones by comparing preoperative and postoperative skull shapes. To divide preoperative and postoperative skulls into two frontal bones, two parietal bones, and the occipital bone, and to estimate the shape deformation of regional cranial bones between the preoperative and postoperative skulls, an age-matched mean-normal skull surface model already divided into five bones is deformed into a preoperative skull, and a deformed mean-normal skull surface model is redeformed into a postoperative skull. To quantify the degree of the expansion and reduction of regional cranial bones after surgery, expansion and reduction indices of the five cranial bones are calculated using the deformable registration as deformation information. The proposed quantification method overcomes the quantification difficulty when using the traditional cephalic index(CI) by analyzing regional cranial bones and provides useful information for quantifying the surgical effects of craniosynostosis patients with symmetric and asymmetric deformities.

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