Sungwon Lee, Keum San Chun, Seungeun Lee, Hyemin Park, Tuan Dinh Le, Joon-Yong Jung
{"title":"腕关节x线摄影中抑制固定装置以改善骨折显像。","authors":"Sungwon Lee, Keum San Chun, Seungeun Lee, Hyemin Park, Tuan Dinh Le, Joon-Yong Jung","doi":"10.1007/s00330-024-11232-2","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This study validates the use of CycleGAN-generated wrist radiographs with digitally removed splints, specifically assessing their impact on fracture visualisation.</p><p><strong>Materials and methods: </strong>We retrospectively collected wrist radiographs from 1748 patients who had imaging before and after splint application at a single institution. The dataset was divided into training (1696 patients, 5353 images) and testing sets (52 patients, 965 images). A CycleGAN-based model was trained to generate splint-free wrist radiographs (generated \"splint-less\") from the original \"splint\" images. A pre-trained fracture detection model (YOLO8s) was used to assess fracture detection performance on three image groups: original \"splint-less\" radiographs, original \"splint\" radiographs, and generated \"splint-less\" radiographs. Two radiologists scored the generated images. Subtraction images quantified overall image alterations. Precision, recall, and F1 scores were used to compare fracture detection performance.</p><p><strong>Results: </strong>CycleGAN effectively generated splint-suppressed radiographs with minimal remaining splint density (< 10% remaining in 97.99%), hardware distortion (< 10% change in 100%), anatomical distortion (< 10% in 99.63%), and fracture lesion changes (< 10% change in 100%). New artefacts were rare (absent in 97.54%). Notably, the fracture detection model achieved higher precision (0.94 vs. 0.92), recall (0.63 vs. 0.5), and F1 score (0.75 vs. 0.65) on the generated \"splint-less\" radiographs compared to the original \"splint\" radiographs, approaching the performance on original \"splint-less\" radiographs (F1 0.71). Furthermore, greater image alterations by CycleGAN correlated with larger improvements in fracture detection.</p><p><strong>Conclusion: </strong>CycleGAN successfully removed splint densities from wrist radiographs with splints.</p><p><strong>Key points: </strong>Question Can CycleGAN (Generative Adversarial Networks), designed for image-to-image translation, generate synthetic \"splint-less\" radiographs to improve fracture visualisation in follow-up radiographs? Findings Removal of splint densities from wrist radiographs using Generative Adversarial Networks preserved anatomical structures and improved the performance of a fracture detection model. Clinical relevance Generated splint-less radiographs can enhance the performance of wrist fracture detection in wrist radiographs, benefiting both human clinicians and AI-powered diagnostic tools.</p>","PeriodicalId":12076,"journal":{"name":"European Radiology","volume":" ","pages":"3418-3428"},"PeriodicalIF":4.7000,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Suppression of immobilisation device on wrist radiography to improve fracture visualisation.\",\"authors\":\"Sungwon Lee, Keum San Chun, Seungeun Lee, Hyemin Park, Tuan Dinh Le, Joon-Yong Jung\",\"doi\":\"10.1007/s00330-024-11232-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Objectives: </strong>This study validates the use of CycleGAN-generated wrist radiographs with digitally removed splints, specifically assessing their impact on fracture visualisation.</p><p><strong>Materials and methods: </strong>We retrospectively collected wrist radiographs from 1748 patients who had imaging before and after splint application at a single institution. The dataset was divided into training (1696 patients, 5353 images) and testing sets (52 patients, 965 images). A CycleGAN-based model was trained to generate splint-free wrist radiographs (generated \\\"splint-less\\\") from the original \\\"splint\\\" images. A pre-trained fracture detection model (YOLO8s) was used to assess fracture detection performance on three image groups: original \\\"splint-less\\\" radiographs, original \\\"splint\\\" radiographs, and generated \\\"splint-less\\\" radiographs. Two radiologists scored the generated images. Subtraction images quantified overall image alterations. Precision, recall, and F1 scores were used to compare fracture detection performance.</p><p><strong>Results: </strong>CycleGAN effectively generated splint-suppressed radiographs with minimal remaining splint density (< 10% remaining in 97.99%), hardware distortion (< 10% change in 100%), anatomical distortion (< 10% in 99.63%), and fracture lesion changes (< 10% change in 100%). New artefacts were rare (absent in 97.54%). Notably, the fracture detection model achieved higher precision (0.94 vs. 0.92), recall (0.63 vs. 0.5), and F1 score (0.75 vs. 0.65) on the generated \\\"splint-less\\\" radiographs compared to the original \\\"splint\\\" radiographs, approaching the performance on original \\\"splint-less\\\" radiographs (F1 0.71). Furthermore, greater image alterations by CycleGAN correlated with larger improvements in fracture detection.</p><p><strong>Conclusion: </strong>CycleGAN successfully removed splint densities from wrist radiographs with splints.</p><p><strong>Key points: </strong>Question Can CycleGAN (Generative Adversarial Networks), designed for image-to-image translation, generate synthetic \\\"splint-less\\\" radiographs to improve fracture visualisation in follow-up radiographs? Findings Removal of splint densities from wrist radiographs using Generative Adversarial Networks preserved anatomical structures and improved the performance of a fracture detection model. Clinical relevance Generated splint-less radiographs can enhance the performance of wrist fracture detection in wrist radiographs, benefiting both human clinicians and AI-powered diagnostic tools.</p>\",\"PeriodicalId\":12076,\"journal\":{\"name\":\"European Radiology\",\"volume\":\" \",\"pages\":\"3418-3428\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2025-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"European Radiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1007/s00330-024-11232-2\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/12/3 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q1\",\"JCRName\":\"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"European Radiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s00330-024-11232-2","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/3 0:00:00","PubModel":"Epub","JCR":"Q1","JCRName":"RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING","Score":null,"Total":0}
Suppression of immobilisation device on wrist radiography to improve fracture visualisation.
Objectives: This study validates the use of CycleGAN-generated wrist radiographs with digitally removed splints, specifically assessing their impact on fracture visualisation.
Materials and methods: We retrospectively collected wrist radiographs from 1748 patients who had imaging before and after splint application at a single institution. The dataset was divided into training (1696 patients, 5353 images) and testing sets (52 patients, 965 images). A CycleGAN-based model was trained to generate splint-free wrist radiographs (generated "splint-less") from the original "splint" images. A pre-trained fracture detection model (YOLO8s) was used to assess fracture detection performance on three image groups: original "splint-less" radiographs, original "splint" radiographs, and generated "splint-less" radiographs. Two radiologists scored the generated images. Subtraction images quantified overall image alterations. Precision, recall, and F1 scores were used to compare fracture detection performance.
Results: CycleGAN effectively generated splint-suppressed radiographs with minimal remaining splint density (< 10% remaining in 97.99%), hardware distortion (< 10% change in 100%), anatomical distortion (< 10% in 99.63%), and fracture lesion changes (< 10% change in 100%). New artefacts were rare (absent in 97.54%). Notably, the fracture detection model achieved higher precision (0.94 vs. 0.92), recall (0.63 vs. 0.5), and F1 score (0.75 vs. 0.65) on the generated "splint-less" radiographs compared to the original "splint" radiographs, approaching the performance on original "splint-less" radiographs (F1 0.71). Furthermore, greater image alterations by CycleGAN correlated with larger improvements in fracture detection.
Conclusion: CycleGAN successfully removed splint densities from wrist radiographs with splints.
Key points: Question Can CycleGAN (Generative Adversarial Networks), designed for image-to-image translation, generate synthetic "splint-less" radiographs to improve fracture visualisation in follow-up radiographs? Findings Removal of splint densities from wrist radiographs using Generative Adversarial Networks preserved anatomical structures and improved the performance of a fracture detection model. Clinical relevance Generated splint-less radiographs can enhance the performance of wrist fracture detection in wrist radiographs, benefiting both human clinicians and AI-powered diagnostic tools.
期刊介绍:
European Radiology (ER) continuously updates scientific knowledge in radiology by publication of strong original articles and state-of-the-art reviews written by leading radiologists. A well balanced combination of review articles, original papers, short communications from European radiological congresses and information on society matters makes ER an indispensable source for current information in this field.
This is the Journal of the European Society of Radiology, and the official journal of a number of societies.
From 2004-2008 supplements to European Radiology were published under its companion, European Radiology Supplements, ISSN 1613-3749.