Introduction and evaluation of a novel multi-camera surface topography system

Michalik, Roman; Knod, M.; Siebers, Hannah Lena; Gatz, Matthias; Dirrichs, Timm; Eschweiler, Jörg; Quack, Valentin; Betsch, Marcel

doi:10.1016/j.gaitpost.2020.06.016

Cited by 5 publications

(17 citation statements)

References 29 publications

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“… Back area Surface area of the dorsal torso, bounded cranially by C7 and caudally by PSIS [ 30 ]. Cross-sectional area ♱ Enclosed area of transverse plane slices taken through the torso at L2, T8, and JN [ 26 ]. Abbreviated XSA.…”

Section: Methodsmentioning

confidence: 99%

“…Abbreviated XSA. Section volume ♱ Three torso volumes were computed, bounded caudally/cranially with axial planes: (1) L2 to T8, (2) XP to JN, and (3) PSIS to JN [ 26 ]. Abbreviated XSV.…”

Section: Methodsmentioning

confidence: 99%

“…Notably, the Formetric 4D video-rasterstereography system is commercially available, and demonstrates good-to-excellent intra- and inter-rater reliability (most ICCs > 0.7) for several surface-topographic measurements in Adolescent Idiopathic Scoliosis (AIS) patients and allows for comparisons over time [ 23 – 25 ]. Crucially, for patients’ self-image, a recent experimental version of the scanner can capture 360° torso reconstructions [ 26 ]. However, measurements require manual landmarking by a trained technician and the system only operates in upright posture, precluding functional analysis (e.g., bending/twisting).…”

Section: Introductionmentioning

confidence: 99%

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Reliability of automated topographic measurements for spine deformity

et al. 2022

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Purpose This study introduces a novel surface-topographic scanning system capable of automatically generating a suite of objective measurements to characterize torso shape. Research Question: what is the reliability of the proposed system for measurement of trunk alignment parameters in patients with adolescent idiopathic scoliosis (AIS) and controls? Methods Forty-six adolescents (26 with AIS and 20 controls) were recruited for a prospective reliability study. A series of angular, volumetric, and area measures were computed from topographic scans in each of three clinically relevant poses using a fully automated processing pipeline. Intraclass correlation coefficients (ICC(2,1)) were computed within (intra-) and between (inter-) raters. Measurements were also performed on a torso phantom. Results Topographic measurements computed on a phantom were highly accurate (mean RMS error 1.7%) compared with CT. For human subjects, intra- and inter-rater reliability were both high (average ICC > 0.90) with intrinsic (pose-independent) measurements having near-perfect reliability (average ICC > 0.98). Conclusion The proposed system is a suitable tool for topographic analysis of AIS; topographic measurements offer an objective description of torso shape that may complement other imaging modalities. Further research is needed to compare topographic findings with gold standard imaging of spinal alignment, e.g., standing radiography. Conclusion: clinical parameters can be reliably measured in a fully automated system, paving the way for objective analysis of symmetry, body shape pre/post-surgery, and tracking of pathology without ionizing radiation.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Reliability of automated topographic measurements for spine deformity

et al. 2022

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“…"Landmarks ": [267,137,261,304,214,154,313,154,239,202,294,192,227,261,295,258,247,286,273,291] "links " : {[ [9,10], [7,8], [5,6], [3,4], [1,2]]},…]} Using this information, heatmaps and vector field maps for each image are labeled for network training.…”

Section: -1-training Dataset Generationmentioning

confidence: 99%

“…These heatmaps are derived from the coordinates of the critical points stored in the JSON file. Assume = ( , ) is the correct position of the j th landmark for the person in the image so that , 2 . To make a heatmap for this landmark, it is necessary to define a neighborhood to the center of this point because it is determined that each heatmap does not contain only one point but also involves a small area, and then the value of the Gaussian function for = ( , ) is calculated in this area.…”

Section: -2-heatmap Generationmentioning

confidence: 99%

Automatic Landmark Detection of Human Back Surface from Depth Images via Deep Learning

Delgarmi

Heravi

et al. 2021

Preprint

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Studying human postural structure is one of the challenging issues among scholars and physicians. The spine is known as the central axis of the body, and due to various genetic and environmental reasons, it could suffer from deformities that cause physical dysfunction and correspondingly reduce people's quality of life. Radiography is the most common method for detecting these deformities and requires monitoring and follow-up until full treatment. This method frequently exposes the patient to X-rays and ionization. Therefore, cancer risk is increased in the patient and could be riskier for children or pregnant women. To prevent this, several solutions have been proposed using topographic data analysis of the human back surface. The purpose of this research is to provide an entirely safe and non-invasive method to examine the spiral structure and its deformities. Hence, it is attempted to find the exact location of anatomical landmarks on the human back surface, which provides useful and practical information about the status of the human postural structure to the physician. In this study, using Microsoft Kinect sensor, the depth images from the human back surface of 105 people were recorded and, our proposed approach - Deep convolution neural network- was used as a model to estimate the location of anatomical landmarks. In network architecture, two learning processes, including landmark position and affinity between the two associated landmarks, are successively performed in two separate branches. This is a bottom-up approach; thus, the runtime complexity is considerably reduced, and then the resulting anatomical points are evaluated concerning manual landmarks marked by the operator as the benchmark. Our results showed that 86.9% of PDJ and 80% of PCK. According to the results, this study was more effective than other methods with more than thousands of training data.

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Depth Dataset Using Microsoft Kinect-v2

Heravi

Delgarmi

et al. 2021

Preprint

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In biomedical imaging studies, numerous methods have been used to capture human data, mostly by using magnetic resonance imaging (MRI) and computed tomography (CT). However, due to being inexpensive and accessibility of Microsoft Kinect, its usage has been significantly increased in recent years. In this study, we aimed to represent the procedure of data acquisition, which includes a set of depth images from individuals’ back surfaces. The goal of image acquisition is to investigate spinal deformities and landmark detection of the back surface. Traditional imaging systems are challenging, most notably because of ionized beams in the data acquisition process, which has not been solved yet. Indeed, noninvasiveness is the most crucial advantage of our study. Our imaging system was set in a dim laboratory, and the University approved the ethical letter of Medical Sciences before data acquisition. After that, the subjects (total 105; 50 women and 55 men) were recruited, and data images were captured from the back surface. Then, we increased the imaging data size by using the augmentation method to use deep learning methods in future works. Finally, this Dataset leads us to the desired output in our study procedure.

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Introduction and evaluation of a novel multi-camera surface topography system

Cited by 5 publications

References 29 publications

Reliability of automated topographic measurements for spine deformity

Reliability of automated topographic measurements for spine deformity

Automatic Landmark Detection of Human Back Surface from Depth Images via Deep Learning

Depth Dataset Using Microsoft Kinect-v2

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