Automated body volume acquisitions from 3D structured-light scanning

Chiu, Chuang‐Yuan; Pease, David; Fawkner, Samantha; Sanders, Ross

doi:10.1016/j.compbiomed.2018.07.016

Cited by 11 publications

(11 citation statements)

References 31 publications

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“…This creates the need for a standardized pose that allows as much leg visibility as possible without repositioning and re-scanning multiple areas. The concept of gathering leg volume measurements from whole-body 3DSI has been addressed in several studies [ 24 , 25 ]. However, there are types of body morphology that do not allow for full leg visibility with this technique.…”

Section: Introductionmentioning

confidence: 99%

Digital Leg Volume Quantification: Precision Assessment of a Novel Workflow Based on Single Capture Three-dimensional Whole-Body Surface Imaging

et al. 2021

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Whole-body three-dimensional surface imaging (3DSI) offers the ability to monitor morphologic changes in multiple areas without the need to individually scan every anatomical region of interest. One area of application is the digital quantification of leg volume. Certain types of morphology do not permit complete circumferential scan of the leg surface. A workflow capable of precisely estimating the missing data is therefore required. We thus aimed to describe and apply a novel workflow to collect bilateral leg volume measurements from whole-body 3D surface scans regardless of leg morphology and to assess workflow precision. For each study participant, whole-body 3DSI was conducted twice successively in a single session with subject repositioning between scans. Paired samples of bilateral leg volume were calculated from the 3D surface data, with workflow variations for complete and limited leg surface visibility. Workflow precision was assessed by calculating the relative percent differences between repeated leg volumes. A total of 82 subjects were included in this study. The mean relative differences between paired left and right leg volumes were 0.73 ± 0.62% and 0.82 ± 0.65%. The workflow variations for completely and partially visible leg surfaces yielded similarly low values. The workflow examined in this study provides a precise method to digitally monitor leg volume regardless of leg morphology. It could aid in objectively comparing medical treatment options of the leg in a clinical setting. Whole-body scans acquired using the described 3DSI routine may allow simultaneous assessment of other changes in body morphology after further validation.

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Section: Introductionmentioning

confidence: 99%

Digital Leg Volume Quantification: Precision Assessment of a Novel Workflow Based on Single Capture Three-dimensional Whole-Body Surface Imaging

et al. 2021

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“…BSA is generally accepted and widely used to assess cardiac function [31]. The most accurate correction, however, would be with the measurement of the patient's volume using a 3-dimensional body scanner [32]. Unfortunately, this technique was not available at our hospital during our study.…”

Section: Discussionmentioning

confidence: 98%

Improvement of Cardiac Function After Roux-en-Y Gastric Bypass in Morbidly Obese Patients Without Cardiac History Measured by Cardiac MRI

et al. 2020

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Purpose Metabolic syndrome in patients with morbid obesity causes a higher cardiovascular morbidity, eventually leading to left ventricular hypertrophy and decreased left ventricular ejection fraction (LVEF). Roux-en-Y gastric bypass (RYGB) is considered the gold standard modality for treatment of morbid obesity and might even lead to improved cardiac function. Our objective is to investigate whether cardiac function in patients with morbid obesity improves after RYGB. Materials and Methods In this single center pilot study, 15 patients with an uneventful cardiac history who underwent RYGB were included from May 2015 to March 2016. Cardiac function was measured by cardiac magnetic resonance imaging (CMRI), performed preoperatively and 3, 6, and 12 months postoperative. LVEF and myocardial mass and cardiac output were measured. Results A total of 13 patients without decreased LVEF preoperative completed follow-up (mean age 37, 48.0 ± 8.8). There was a significant decrease of cardiac output 12 months postoperative (8.3 ± 1.8 preoperative vs. 6.8 ± 1.8 after 12 months, P = 0.001). Average myocardial mass declined by 15.2% (P < 0.001). After correction for body surface area (BSA), this appeared to be nonsignificant (P = 0.36). There was a significant improvement of LVEF/BSA at 6 and 12 months postoperative (26.2 ± 4.1 preoperative vs. 28.4 ± 3.4 and 29.2 ± 3.6 respectively, both P = 0.002). Additionally, there was a significant improvement of stroke volume/BSA 12 months after surgery (45.8 ± 8.0 vs. 51.9 ± 10.7, P = 0.033). Conclusion RYGB in patients with morbid obesity with uneventful history of cardiac disease leads to improvement of cardiac function.

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“…The raw 3D scanning data in the WEAR dataset contains holes and noise as shown in Figure 1A . Automatic post‐processing techniques adapted from the previous development 30 were applied to obtain watertight 3D models from the large datasets for BV acquisitions. The test–retest of whole BV obtained from the automatic post‐processing techniques are less than 1 L. 30 The automatic technique enables accurate BV compared with manual post‐processing 30 the acquired BVs (error < 1 L) were thus used as reference values for validation.…”

Section: Methodsmentioning

confidence: 99%

Modification and refinement of three‐dimensional reconstruction to estimate body volume from a simulated single‐camera image

Chiu

Dunn

Heller

et al. 2022

Obesity Science & Practice

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Objective: Body volumes (BV) are used for calculating body composition to perform obesity assessments. Conventional BV estimation techniques, such as underwater weighing, can be difficult to apply. Advanced machine learning techniques enable multiple obesity-related body measurements to be obtained using a single-camera image; however, the accuracy of BV calculated using these techniques is unknown. This study aims to adapt and evaluate a machine learning technique, synthetic training for real accurate pose and shape (STRAPS), to estimate BV. Methods:The machine learning technique, STRAPS, was applied to generate threedimensional (3D) models from simulated two-dimensional (2D) images; these 3D models were then scaled with body stature and BV were estimated using regression models corrected for body mass. A commercial 3D scan dataset with a wide range of participants (n = 4318) was used to compare reference and estimated BV data. Results:The developed methods estimated BV with small relative standard errors of estimation (<7%) although performance varied when applied to different groups.The BV estimated for people with body mass index (BMI) < 30 kg/m 2 (1.9% for males and 1.8% for females) were more accurate than for people with BMI ≥ 30 kg/m 2 (6.9% for males and 2.4% for females). Conclusions:The developed method can be used for females and males with BMI < 30 kg/m 2 in BV estimation and could be used for obesity assessments at home or clinic settings.

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Automated body volume acquisitions from 3D structured-light scanning

Cited by 11 publications

References 31 publications

Digital Leg Volume Quantification: Precision Assessment of a Novel Workflow Based on Single Capture Three-dimensional Whole-Body Surface Imaging

Digital Leg Volume Quantification: Precision Assessment of a Novel Workflow Based on Single Capture Three-dimensional Whole-Body Surface Imaging

Improvement of Cardiac Function After Roux-en-Y Gastric Bypass in Morbidly Obese Patients Without Cardiac History Measured by Cardiac MRI

Modification and refinement of three‐dimensional reconstruction to estimate body volume from a simulated single‐camera image

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