Low-Cost 3d Devices and Laser Scanners Comparison for the Application in Orthopedic Centres

Redaelli, Davide Felice; Barsanti, Sara Gonizzi; Fraschini, Paolo; Biffi, Emilia; Colombo, Giorgio

doi:10.5194/isprs-archives-xlii-2-953-2018

Cited by 14 publications

(10 citation statements)

References 15 publications

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“…The Minolta laser scanner showed a behaviour in line with the previous results presented in [29]. Figure 6 shows that an improvement could be obtained with a proper calibration in order to remove the vertical stripes effect and the horizontal bowing.…”

Section: Flat Planesupporting

confidence: 84%

“…Three low-cost general-purpose devices were evaluated in this work: the Kinect 1 and the Kinect 2 by Microsoft and the Structure Sensor by Occipital. In addition, the data regarding the O&P Scan by Rodin4D, deriving from our previous work [29] of only the plane and the mannequin parts, were included for comparison. Medium-and high-level scanners (Konica Minolta Vivid 9i, GOM ATOS II 400, ARTEC LEO and NextEngine Ultra HD 3D scanner) were used for selecting which to take as the reference for comparing results on the mannequin parts, based on the results on the standard objects.…”

Section: Devicesmentioning

confidence: 99%

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Comparison of geometrical accuracy of active devices for 3D orthopaedic reconstructions

Redaelli

Barsanti

Biffi

et al. 2021

Int J Adv Manuf Technol

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The use of 3D digitizing tools is becoming the base for subject-specific products, such as the orthopaedic production process of orthoses and prostheses. This paper aims at comparing the metrological behaviour of low-cost devices (Kinect 1 and 2 by Microsoft, Structure Sensor by Occipital) and high-resolution active sensors (O&P Scan by Rodin4D, NextEngine Ultra HD, Konica Minolta Vivid 9i, GOM ATOS II 400 and Artec Leo) for the survey of human body parts. A calibrated flat plane and a test-field composed of eight calibrated spheres of different radii and placed at different heights were used to evaluate the standard quality parameters (flatness, probing errors in form and size and the standard deviation) for each device as recommended by the VDI/VDE 2634 guidelines. Subsequently, three different parts of a mannequin were surveyed as samples of human body parts. The results demonstrated the higher accuracy of fixed devices with respect to handheld ones, among which Artec Leo and Structure Sensor provided a satisfying level of accuracy for the orthopaedic application. Moreover, the handheld devices enabled performing a fast reconstruction of the mannequin parts in about 20 s, which is acceptable for a person that has to remain as still as possible. For this reason, the Structure Sensor was further tested with five motion approaches which identified that smooth motion provides the lowest deviation and higher reliability. The work demonstrated the appropriateness of handheld devices for the orthopaedic application requirements in terms of speed, accuracy and costs.

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Section: Flat Planesupporting

confidence: 84%

Section: Devicesmentioning

confidence: 99%

Comparison of geometrical accuracy of active devices for 3D orthopaedic reconstructions

Redaelli

Barsanti

Biffi

et al. 2021

Int J Adv Manuf Technol

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show abstract

“…In [ 89 ], a comparison between four low-cost 3D scanners (Kinect V1 and V2, the Structure Sensor and the O&P Scan Rodin4D) and a high-end laser scanner, Minolta Vivid 9i (Konica Minolta), was carried out for the reconstruction of human body parts. Three anatomical parts of a mannequin (a hand, a thigh, and a chest) were used to investigate the results on different dimensions and detail levels.…”

Section: 3d Scanner Architectures For the Reconstruction Of Upper mentioning

confidence: 99%

“… Results obtained by the different devices compared in [ 89 ]. The model acquired with the Minolta system has been used as a reference for the comparison of all the other devices.…”

Section: Figurementioning

confidence: 99%

Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review

Paoli

Neri

Razionale

et al. 2020

Sensors

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3D digital models of the upper limb anatomy represent the starting point for the design process of bespoke devices, such as orthoses and prostheses, which can be modeled on the actual patient’s anatomy by using CAD (Computer Aided Design) tools. The ongoing research on optical scanning methodologies has allowed the development of technologies that allow the surface reconstruction of the upper limb anatomy through procedures characterized by minimum discomfort for the patient. However, the 3D optical scanning of upper limbs is a complex task that requires solving problematic aspects, such as the difficulty of keeping the hand in a stable position and the presence of artefacts due to involuntary movements. Scientific literature, indeed, investigated different approaches in this regard by either integrating commercial devices, to create customized sensor architectures, or by developing innovative 3D acquisition techniques. The present work is aimed at presenting an overview of the state of the art of optical technologies and sensor architectures for the surface acquisition of upper limb anatomies. The review analyzes the working principles at the basis of existing devices and proposes a categorization of the approaches based on handling, pre/post-processing effort, and potentialities in real-time scanning. An in-depth analysis of strengths and weaknesses of the approaches proposed by the research community is also provided to give valuable support in selecting the most appropriate solution for the specific application to be addressed.

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“…To this day, this issue is a hindrance to business models centered on 3D scanning such as the novel 3Dscan-to-3Dprint business model; inadequate scans must be manually corrected by the operators of common software such as MeshLab TM , MSoft TM , MeshMixer TM and Geomagic TM [24], [25]. The use of these software is an offline process that is time consuming.…”

Section: Introductionmentioning

confidence: 99%

Shapeshift 3D Repair - A Fully Automated and Unsupervised Cloud API for the Reliable Reconstruction of Raw 3D Surface Scans Data

Laurin¹,

Béland²,

Borduas³

2019

Proceedings of 3DBODY.TECH 2019 - 10th International Conference and Exhibition on 3D Body Scanning and Processing Technologies,

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This paper presents a study to quantify the reliability of the automatic reconstruction tool Shapeshift 3D Repair TM to create watertight, genus 0, precise and accurate 3D surface scan of the human body. Our methodology uses a precise baseline 3D scan acquired from a full body 3D scanner as an input of a scanning process simulator that emulates the properties of a common 3D scanner, the Structure TM by Occipital TM , and the behavior of a typical untrained handheld 3D scanner operator. The output of the simulator is a raw scan (noisy and incomplete). Afterward, the raw scan is fed to Shapeshift 3D Repair TM which outputs a reconstructed scan. We express the reliability of the process in terms of Standard Error of Measurement (SEM). Using the girth difference between the baseline scan and the reconstructed scan, we express the compatibility in terms of Signed Mean Difference (Bias) and Mean Absolute Error (MAE). We compare our results with common reconstruction methods found in the literature and with other studies about the reliability of 3D Scanning, Plaster Casting and Traditional Anthropometry. Context: To create custom medical devices and wearables, the patient's 3D geometry can be acquired using a 3D scanner. The raw 3D scans require post-processing as they are often noisy and incomplete. While organizations using 3D scanners put in place training programs, scans are often of poor quality. To this day, this issue is a hindrance to business models centered on 3D scanning such as the novel 3Dscan-to-3Dprint business model; inadequate scans must be manually corrected by the operators, which is a time-consuming offline process. The study is focused on the simulation of the scanning process and the scan reconstruction of the knee. Results: A fully automated and unsupervised cloud processing service for the reconstruction of the knee has been implemented and is ready to be tested by users and vendors of 3D scanners. Reconstructed scans exhibit leg, knee and max thigh girth error under 0.1 cm, 0.3 cm and 0.4 cm respectively with 95% confidence level while producing properly defined surface that are manifold, genus 0, have good triangle aspect ratio, and have a single surface. With the recent boom of devices featuring an embedded 3D scanner, we believe that in due time, our technology can be accessible to millions of users without the needs of industry-specific hardware or skills.

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Low-Cost 3d Devices and Laser Scanners Comparison for the Application in Orthopedic Centres

Cited by 14 publications

References 15 publications

Comparison of geometrical accuracy of active devices for 3D orthopaedic reconstructions

Comparison of geometrical accuracy of active devices for 3D orthopaedic reconstructions

Sensor Architectures and Technologies for Upper Limb 3D Surface Reconstruction: A Review

Shapeshift 3D Repair - A Fully Automated and Unsupervised Cloud API for the Reliable Reconstruction of Raw 3D Surface Scans Data

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