Fabian N. Necker, David J. Cholok, Marc J. Fischer, Kyle Gifford, Chris Le Castillo, Michael Scholz, Michael Januszyk, Christoph W. Leuze, Bruce L. Daniel, Arash Momeni
{"title":"利用照片测量腹部皮肤表面距离用于穿孔器测绘分析——3d打印diep -皮瓣模型的验证研究。","authors":"Fabian N. Necker, David J. Cholok, Marc J. Fischer, Kyle Gifford, Chris Le Castillo, Michael Scholz, Michael Januszyk, Christoph W. Leuze, Bruce L. Daniel, Arash Momeni","doi":"10.1002/rcs.70108","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>We present a novel method for accurately measuring skin-surface distances using standard smartphone photos and Photoshop, validated on 3D-printed DIEP-flap models and on calibration grid-patterns.</p>\n </section>\n \n <section>\n \n <h3> Materials and Methods</h3>\n \n <p>Distance measurements are acquired in Photoshop in a calibration plane between dots on a grid-pattern as well as between perforators on photos of 3D-printed models and compared against ground-truth. Margins of errors are calculated from fitted linear models.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Submillimeter accuracy can be achieved within errors of ±0.45 mm (80% probability) and ±0.8 mm (95% probability) for measuring distances on the dot-grid. On the 3D-printed DIEP-models, distance measurements are accurate within ±1.75 mm (80% probability) and ±3.1 mm (95% probability).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>We introduce a simple yet highly accurate technique to measure skin-surface distances using normal photos. Depending on the scenario, submillimeter or conservatively very low millimetre errors can be achieved, sufficiently accurate for clinical use, whilst maintaining topographic relationships of the measurements.</p>\n </section>\n </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"21 5","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12444031/pdf/","citationCount":"0","resultStr":"{\"title\":\"Measuring Abdominal Skin-Surface Distances Using Photos for Perforator Mapping Analysis—A Validation Study on 3D-Printed DIEP-Flap Models\",\"authors\":\"Fabian N. Necker, David J. Cholok, Marc J. Fischer, Kyle Gifford, Chris Le Castillo, Michael Scholz, Michael Januszyk, Christoph W. Leuze, Bruce L. Daniel, Arash Momeni\",\"doi\":\"10.1002/rcs.70108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Background</h3>\\n \\n <p>We present a novel method for accurately measuring skin-surface distances using standard smartphone photos and Photoshop, validated on 3D-printed DIEP-flap models and on calibration grid-patterns.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Materials and Methods</h3>\\n \\n <p>Distance measurements are acquired in Photoshop in a calibration plane between dots on a grid-pattern as well as between perforators on photos of 3D-printed models and compared against ground-truth. Margins of errors are calculated from fitted linear models.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Submillimeter accuracy can be achieved within errors of ±0.45 mm (80% probability) and ±0.8 mm (95% probability) for measuring distances on the dot-grid. On the 3D-printed DIEP-models, distance measurements are accurate within ±1.75 mm (80% probability) and ±3.1 mm (95% probability).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>We introduce a simple yet highly accurate technique to measure skin-surface distances using normal photos. 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Measuring Abdominal Skin-Surface Distances Using Photos for Perforator Mapping Analysis—A Validation Study on 3D-Printed DIEP-Flap Models
Background
We present a novel method for accurately measuring skin-surface distances using standard smartphone photos and Photoshop, validated on 3D-printed DIEP-flap models and on calibration grid-patterns.
Materials and Methods
Distance measurements are acquired in Photoshop in a calibration plane between dots on a grid-pattern as well as between perforators on photos of 3D-printed models and compared against ground-truth. Margins of errors are calculated from fitted linear models.
Results
Submillimeter accuracy can be achieved within errors of ±0.45 mm (80% probability) and ±0.8 mm (95% probability) for measuring distances on the dot-grid. On the 3D-printed DIEP-models, distance measurements are accurate within ±1.75 mm (80% probability) and ±3.1 mm (95% probability).
Conclusions
We introduce a simple yet highly accurate technique to measure skin-surface distances using normal photos. Depending on the scenario, submillimeter or conservatively very low millimetre errors can be achieved, sufficiently accurate for clinical use, whilst maintaining topographic relationships of the measurements.
期刊介绍：
The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.
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