Wound perimeter, area, and volume measurement based on laser 3D and color acquisition

Accreditation of the handheld 3-dimensional scanner and conventional photo images for area measurement

Accurate assessment of wound areas is crucial in making therapeutic decisions, as the prognosis and changes in the size of the wound over time play a significant role. An ideal assessment method should possess qualities such as speed, affordability, accuracy, user-friendliness for both patients and healthcare professionals, and suitability for daily clinical practice. This study aims to introduce a handheld 3-dimensional (3D) scanner and evaluate its accuracy in measuring wound areas. Engineers from the Industrial Technology Research Institute in Taiwan developed a handheld 3D scanner with the intention of extending its application to the medical field. A project was conducted to validate the accuracy of this 3D scanner. We utilized a smartphone (Asus ZenFone 2 with a 13-million-pixel rear camera), a digital single-lens reflex digital camera (Nikon, D5000, Tokyo, Japan), and the 3D scanner to repeatedly measure square papers of known size that were affixed to the curved surface of life-size facial mask or medical teaching breast models. The "Image J" software was employed for 2-dimensional image measurements, while the "3D Edit" software was used to assess the "area of interest" on 3D objects. By using square papers with predetermined dimensions, the measurement-associated error rate (ER) could be calculated for each image. Three repeated measurements were performed using the "Image J" software for each square paper. The ERs of the 3D scan images were all below 3%, with an average ER of 1.64% in this study. The close-up mode of the smartphone exhibited the highest ER. It was observed that as the area increased, the ER also increased in the digital single-lens reflex camera group. The extension distortion effect caused by the wide-angle lens on the smartphone may increase the ER. However, the definition of a healthy skin edge may vary, and different algorithms for calculating the measurement area are employed in various 3D measurement software. Therefore, further validation of their accuracy for medical purposes is necessary. Effective communication with software engineers and discussions on meeting clinical requirements are crucial steps in enhancing the functionality of the 3D scanner.

2D/3D Wound Segmentation and Measurement Based on a Robot-Driven Reconstruction System

Chronic wounds, are a worldwide health problem affecting populations and economies as a whole. With the increase in age-related diseases, obesity, and diabetes, the costs of chronic wound healing will further increase. Wound assessment should be fast and accurate in order to reduce possible complications and thus shorten the wound healing process. This paper describes an automatic wound segmentation based on a wound recording system built upon a 7-DoF robot arm with an attached RGB-D camera and high-precision 3D scanner. The developed system represents a novel combination of 2D and 3D segmentation, where the 2D segmentation is based on the MobileNetV2 classifier and the 3D component is based on the active contour model, which works on the 3D mesh to further refine the wound contour. The end output is the 3D model of only the wound surface without the surrounding healthy skin and geometric parameters in the form of perimeter, area, and volume.

Performance of portable objective wound assessment tools: a systematic review

OBJECTIVE

Accurate wound assessment is crucial for determining the progression of healing and guides treatment strategies. Portable wound assessment devices can be useful in providing an accurate evaluation in the community where most cases are treated. The objective of this review was to compare the performance of various portable wound assessment techniques used for wound healing assessment described in the literature.

METHOD

In April 2020, electronic databases were searched, using appropriate search terms, for all available publications on the use of portable wound assessment devices on human and artificial wounds. The primary outcome was the reliability and reproducibility of measurement while the secondary outcome was the feasibility of the instrument. All studies underwent quality assessment of diagnostic accuracy studies (QUADAS) to examine the quality of data.

RESULTS

A total of 129 articles were identified and 24 were included in the final review; 17 articles discussed two-dimensional (2D) devices; three articles discussed three-dimensional (3D) devices; and four articles discussed application-based devices. Most studies (n=8) reported on a 2D device that had an ICC of 0.92-0.99 for area measurement and a coefficient of variance of 3.1% with an error of 2.3% in human wounds and 1.55-3.7% in artificial wounds. The inter/intra observer reliability was 0.998 and 0.985, respectively with a scan time of two minutes per wound. The median QUADAS score was 12.

CONCLUSION

Based on the presented evidence, 2D-based portable wound assessment devices were the most studied and demonstrated good performance. Further studies are required for 3D and application-based measurement instruments.

Image-Based Artificial Intelligence in Wound Assessment: A Systematic Review

Significance: Accurately predicting wound healing trajectories is difficult for wound care clinicians due to the complex and dynamic processes involved in wound healing. Wound care teams capture images of wounds during clinical visits generating big datasets over time. Developing novel artificial intelligence (AI) systems can help clinicians diagnose, assess the effectiveness of therapy, and predict healing outcomes. Recent Advances: Rapid developments in computer processing have enabled the development of AI-based systems that can improve the diagnosis and effectiveness of therapy in various clinical specializations. In the past decade, we have witnessed AI revolutionizing all types of medical imaging like X-ray, ultrasound, computed tomography, magnetic resonance imaging, etc., but AI-based systems remain to be developed clinically and computationally for high-quality wound care that can result in better patient outcomes. Critical Issues: In the current standard of care, collecting wound images on every clinical visit, interpreting and archiving the data are cumbersome and time consuming. Commercial platforms are developed to capture images, perform wound measurements, and provide clinicians with a workflow for diagnosis, but AI-based systems are still in their infancy. This systematic review summarizes the breadth and depth of the most recent and relevant work in intelligent image-based data analysis and system developments for wound assessment. Future Directions: With increasing availabilities of massive data (wound images, wound-specific electronic health records, etc.) as well as powerful computing resources, AI-based digital platforms will play a significant role in delivering data-driven care to people suffering from debilitating chronic wounds.

Home Care and the Effects of Hypericum tetrapterum Oil Extract in the Treatment of Chronic Wounds During the COVID-19 Pandemic

The COVID-19 pandemic and the need for social distancing brought about sudden changes in the health system and treatment strategies. Patients with chronic wounds were affected by these changes and had limited access to professional treatment in hospitals. They were at a higher risk of infection with COVID-19 due to comorbidities and advanced age. The aim of the study was to develop an appropriate protocol for the in-home treatment of chronic wounds due to the COVID-19 pandemic when access to hospitals is limited and the risk of infection for these patients is high. In our case, Hypericum tetrapterum oil extract was applied for four months on a volunteer, a 78-year-old male patient with a chronic wound, additionally infected with Pseudomonas aeruginosa and comorbidities. His healing status was monitored by measuring the wound size and microbiological analysis at certain intervals. The scab of wound DPHR² (right lower leg chronic wound 2), with its diameters of d1 (40 mm) and d2 (20 mm), fell off after 22 days of the first Hypericum tetrapterum oil extract application. The scab of wound DPHR1 (right lower leg chronic wound 1), with its diameters of d1 (74 mm) and d2 (35 mm), fell off after two and a half months of treatment with Hypericum tetrapterum oil extract. The results of our study indicated that Hypericum tetrapterum oil extract has a significant wound-healing potential and might be used as traditional medicine in the treatment of chronic wounds.

Design and Validation of a Custom-Made Laboratory Hyperspectral Imaging System for Biomedical Applications Using a Broadband LED Light Source

Hyperspectral imaging (HSI) is a promising optical modality that is already being used in numerous applications. Further expansion of the capabilities of HSI depends on the modularity and versatility of the systems, which would, inter alia, incorporate profilometry, fluorescence imaging, and Raman spectroscopy while following a rigorous calibration and verification protocols, thus offering new insights into the studied samples as well as verifiable, quantitative measurement results applicable to the development of quantitative metrics. Considering these objectives, we developed a custom-made laboratory HSI system geared toward biomedical applications. In this report, we describe the design, along with calibration, characterization, and verification protocols needed to establish such systems, with the overall goal of standardization. As an additional novelty, our HSI system uses a custom-built broadband LED-based light source for reflectance imaging, which is particularly important for biomedical applications due to the elimination of sample heating. Three examples illustrating the utility and advantages of the integrated system in biomedical applications are shown. Our attempt presents both the development of a custom-based laboratory HSI system with novel LED light source as well as a framework which may improve technological standards in HSI system design.

Hyperspectral evaluation of vasculature in induced peritonitis mouse models

Imaging of blood vessel structure in combination with functional information about blood oxygenation can be important in characterizing many different health conditions in which the growth of new vessels contributes to the overall condition. In this paper, we present a method for extracting comprehensive maps of the vasculature from hyperspectral images that include tissue and vascular oxygenation. We also show results from a preclinical study of peritonitis in mice. First, we analyze hyperspectral images using Beer-Lambert exponential attenuation law to obtain maps of hemoglobin species throughout the sample. We then use an automatic segmentation algorithm to extract blood vessels from the hemoglobin map and combine them into a vascular structure-oxygenation map. We apply this methodology to a series of hyperspectral images of the abdominal wall of mice with and without induced peritonitis. Peritonitis is an inflammation of peritoneum that leads, if untreated, to complications such as peritoneal sclerosis and even death. Characteristic inflammatory response can also be accompanied by changes in vasculature, such as neoangiogenesis. We demonstrate a potential application of the proposed segmentation and processing method by introducing an abnormal tissue fraction metric that quantifies the amount of tissue that deviates from the average values of healthy controls. It is shown that the proposed metric successfully discriminates between healthy control subjects and model subjects with induced peritonitis and has a high statistical significance.

Automatic Robot-Driven 3D Reconstruction System for Chronic Wounds

Chronic wounds, or wounds that are not healing properly, are a worldwide health problem that affect the global economy and population. Alongside with aging of the population, increasing obesity and diabetes patients, we can assume that costs of chronic wound healing will be even higher. Wound assessment should be fast and accurate in order to reduce the possible complications, and therefore shorten the wound healing process. Contact methods often used by medical experts have drawbacks that are easily overcome by non-contact methods like image analysis, where wound analysis is fully or partially automated. This paper describes an automatic wound recording system build upon 7 DoF robot arm with attached RGB-D camera and high precision 3D scanner. The developed system presents a novel NBV algorithm that utilizes surface-based approach based on surface point density and discontinuity detection. The system was evaluated on multiple wounds located on medical models as well as on real patents recorded in clinical medical center.

The UTrack framework for segmenting and measuring dermatological ulcers through telemedicine

Chronic dermatological ulcers cause great discomfort to patients, and while monitoring the size of wounds over time provides significant clues about the healing evolution and the clinical condition of patients, the lack of practical applications in existing studies impairs users' access to appropriate treatment and diagnosis methods. We propose the UTrack framework to help with the acquisition of photos, the segmentation and measurement of wounds, the storage of photos and symptoms, and the visualization of the evolution of ulcer healing. UTrack-App is a mobile app for the framework, which processes images taken by standard mobile device cameras without specialized equipment and stores all data locally. The user manually delineates the regions of the wound and the measurement object, and the tool uses the proposed UTrack-Seg segmentation method to segment them. UTrack-App also allows users to manually input a unit of measurement (centimeter or inch) in the image to improve the wound area estimation. Experiments show that UTrack-Seg outperforms its state-of-the-art competitors in ulcer segmentation tasks, improving F-Measure by up to 82.5% when compared to superpixel-based approaches and up to 19% when compared to Deep Learning ones. The method is unsupervised, and it semi-automatically segments real-world images with 0.9 of F-Measure, on average. The automatic measurement outperformed the manual process in three out of five different rulers. UTrack-App takes at most 30 s to perform all evaluation steps over high-resolution images, thus being well-suited to analyze ulcers using standard mobile devices.

Matrix metalloproteinase (MMP)-1 and MMP-2, but not COX-2 serve as additional predictors for chronic venous ulcer healing

Chronic venous ulcers affect 1% of the adult population and are associated with a marked reduction in quality of life, especially if healing is prolonged. Several matrix metalloproteinases (MMPs) appear to be involved in the pathophysiology of chronic venous ulcer healing, but their exact role is still unclear. Cyclooxygenase-2 (COX-2) is an important enzyme in prostanoid synthesis, induced during inflammation in chronic venous ulcer. The first aim of our study was to compare the expression of MMP-1, MMP-2, and COX-2 in wound tissue to that in normal skin. The second aim was to observe the expression of the above factors in 29 chronic venous ulcers in 22 patients at the beginning and 4 weeks later in relation to healing rates and final healing outcome after 24 weeks. The enrolled population was divided into two groups, healed and non-healed wounds after 24 weeks. The intensity of expression of MMP-1, MMP-2 and COX-2 was assessed for each ulcer in paired wound biopsy samples and wound size measurements using laser triangulation at the beginning and after 4 weeks of observation. Initial healing rates in the first 4 weeks were calculated and proved to be an important predictive factor of healing in 24 weeks. Decreases in MMP-1 and MMP-2 after 4 weeks of observation were distinct, positive predictors for ulcer healing. Healing odds were 3.7 times higher for a decrease in MMP-1 and 2.1 times higher for a decrease in MMP-2 compared to the healing odds for a non-decrease in MMP-1 and MMP-2. In conclusion, a decrease in MMP-1 and MMP-2, but not COX-2, in wound biopsy samples after 4 weeks of observation can predict better healing of chronic venous ulcer.

Hyperspectral evaluation of peritoneal fibrosis in mouse models

Analysis of morphological changes of the peritoneal membrane is an essential part of animal studies when investigating molecular mechanisms involved in the development of peritoneal fibrosis or testing the effects of potential therapeutic agents. Current methods, such as histology and immunohistochemistry, require time consuming sample processing and analysis and result in limited spatial information. In this paper we present a new method to evaluate structural and chemical changes in an animal model of peritoneal fibrosis that is based on hyperspectral imaging and a model of light transport. The method is able to distinguish between healthy and diseased subjects based on morphological as well as physiological parameters such as blood and scattering parameters. Furthermore, it enables evaluation of changes, such as degree of inflammation and fibrosis, that are closely related to histological findings.

Curvature and height corrections of hyperspectral images using built-in 3D laser profilometry

Optical imaging systems use a light source that illuminates a sample and a photodetector that detects light reflected from or transmitted through the sample. The sample surface curvature, surface-to-camera distance, and illumination-source-to-surface distance significantly affect the measured signal, resulting in image artifacts. To correct the images, a three-dimensional (3D) profilometry system was used to obtain 3D surface information. The 3D information enables image correction using Lambert cosine law and height correction. In this study, the feasibility of the correction method for push-broom hyperspectral imaging of three different objects is presented. Results show a significant reduction of image artifacts, making further image analysis more accurate and robust. The presented 3D profilometry method is applicable to all push-broom imaging systems and the described correction procedure can be applied to all spectral or monochromatic imaging systems.

Validation of three-dimensional wound measurements using a novel 3D-WAM camera

To monitor wound healing, it is essential to obtain accurate and reliable wound measurements. Various methods have been used to measure wound size including three-dimensional (3D) measurement devices enabling wound assessment from a volume perspective. However, the currently available methods are inaccurate, costly, or complicated to use. As a consequence, we have developed a 3D-wound assessment monitor (WAM) camera, which is able to measure wound size in three-dimension and to assess wound characteristics. The aim of the study was to assess the intrarater and interrater reliability of the 3D wound measurements using the 3D camera and to compare these with traditional measurement methods. Four raters measured 48 wounds using the 3D camera, digital imaging method (2D area), and gel injection into the wound cavity (volume). The data were analyzed using linear mixed effect model. Intraclass and interclass correlation coefficient (ICC) and Bland-Altman plots were used to assess intrarater and interrater reliability for the 3D camera and agreement between the methods. The Bland-Altman plots for intrarater reliability showed minor differences between the measurements, especially the 3D area and perimeter measurements. Moreover, ICCs were very high for both the intrarater and interrater reliability for the 2D area, 3D area, and perimeter measurements (ICCs > 0.99), although slightly lower for the volume measurements (ICC = 0.946-0.950). Finally, a high agreement was found between the 3D camera and the traditional methods (2D area and volume) assessed by narrow 95% prediction intervals and high ICCs above 0.97. In conclusion, the 3D-WAM camera is an accurate and reliable method, which is useful for several types of wounds. However, the volume measurements were primarily useful in large, deep wounds. Moreover, the 3D images are based on digital technology and therefore carry the possibility for use in remote settings.

Ways to increase precision and accuracy of wound area measurement using smart devices: Advanced app Planimator

Introduction

Wound surface area measurement is important as therapeutic decisions may depend on the change of wound surface area over time. Digital planimetry is a popular technique in wound area measurement. It is accurate and repeatable when calibration is performed with 2 rulers placed at opposite sides of a wound. The aim of the current study was improving accuracy and precision of wound area measurement using capabilities of smart devices.

Methods

The correction of area measurement based on calculated camera tilt angle and the calculation of calibration coefficient of linear dimensions as weighted average were proposed. These and other improvements were applied in the Planimator app for Android, which was then used in the study. Accuracy and precision of the Planimator app were compared to the Visitrak device, the SilhouetteMobile device, the AreaMe software, and to the digital planimetry based on 2-ruler calibration with pictures taken by the smartphone, compact, and D-SLR cameras. Areas of 40 wound shapes of area ranged from 0.14 to 31.72 cm² were measured with each device. Medians of relative errors (REs) were compared in the accuracy tests and standard deviations (SDs) of relative differences (RDs) were compared in the tests of precision.

Results

The median of REs for the Planimator app was not significantly different from the medians of REs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the medians of REs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The SD of RDs for the Planimator app was not significantly different from the SDs of RDs for the digital planimetry based on pictures from the compact or D-SLR cameras, but it was significantly lower than the SDs of RDs for the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera. The Planimator app installed at a smartphone revealed to be 2-fold more accurate and 1.5-fold more precise than the measurements with using ImageJ software based on pictures taken with the same smartphone.

Conclusions

The Planimator app occurred to have the same accuracy and precision as measurements with digital planimetry with 2-ruler calibration and based on pictures from a compact camera or a D-SLR camera. This app showed better accuracy and precision than the Visitrak and SilhouetteMobile devices, the AreaMe software and the digital planimetry based on pictures from a smartphone camera.