{"title":"利用对术中气胸变形的自监督离线学习,实现内窥镜相机图像的二维/三维可变形配准","authors":"","doi":"10.1016/j.compmedimag.2024.102418","DOIUrl":null,"url":null,"abstract":"<div><p>Shape registration of patient-specific organ shapes to endoscopic camera images is expected to be a key to realizing image-guided surgery, and a variety of applications of machine learning methods have been considered. Because the number of training data available from clinical cases is limited, the use of synthetic images generated from a statistical deformation model has been attempted; however, the influence on estimation caused by the difference between synthetic images and real scenes is a problem. In this study, we propose a self-supervised offline learning framework for model-based registration using image features commonly obtained from synthetic images and real camera images. Because of the limited number of endoscopic images available for training, we use a synthetic image generated from the nonlinear deformation model that represents possible intraoperative pneumothorax deformations. In order to solve the difficulty in estimating deformed shapes and viewpoints from the common image features obtained from synthetic and real images, we attempted to improve the registration error by adding the shading and distance information that can be obtained as prior knowledge in the synthetic image. Shape registration with real camera images is performed by learning the task of predicting the differential model parameters between two synthetic images. The developed framework achieved registration accuracy with a mean absolute error of less than 10 mm and a mean distance of less than 5 mm in a thoracoscopic pulmonary cancer resection, confirming improved prediction accuracy compared with conventional methods.</p></div>","PeriodicalId":50631,"journal":{"name":"Computerized Medical Imaging and Graphics","volume":null,"pages":null},"PeriodicalIF":5.4000,"publicationDate":"2024-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0895611124000958/pdfft?md5=3066bd94344d2f3879bdc4b7435a2810&pid=1-s2.0-S0895611124000958-main.pdf","citationCount":"0","resultStr":"{\"title\":\"2D/3D deformable registration for endoscopic camera images using self-supervised offline learning of intraoperative pneumothorax deformation\",\"authors\":\"\",\"doi\":\"10.1016/j.compmedimag.2024.102418\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Shape registration of patient-specific organ shapes to endoscopic camera images is expected to be a key to realizing image-guided surgery, and a variety of applications of machine learning methods have been considered. Because the number of training data available from clinical cases is limited, the use of synthetic images generated from a statistical deformation model has been attempted; however, the influence on estimation caused by the difference between synthetic images and real scenes is a problem. In this study, we propose a self-supervised offline learning framework for model-based registration using image features commonly obtained from synthetic images and real camera images. Because of the limited number of endoscopic images available for training, we use a synthetic image generated from the nonlinear deformation model that represents possible intraoperative pneumothorax deformations. In order to solve the difficulty in estimating deformed shapes and viewpoints from the common image features obtained from synthetic and real images, we attempted to improve the registration error by adding the shading and distance information that can be obtained as prior knowledge in the synthetic image. Shape registration with real camera images is performed by learning the task of predicting the differential model parameters between two synthetic images. The developed framework achieved registration accuracy with a mean absolute error of less than 10 mm and a mean distance of less than 5 mm in a thoracoscopic pulmonary cancer resection, confirming improved prediction accuracy compared with conventional methods.</p></div>\",\"PeriodicalId\":50631,\"journal\":{\"name\":\"Computerized Medical Imaging and Graphics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2024-07-19\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0895611124000958/pdfft?md5=3066bd94344d2f3879bdc4b7435a2810&pid=1-s2.0-S0895611124000958-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computerized Medical Imaging and Graphics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0895611124000958\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computerized Medical Imaging and Graphics","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0895611124000958","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
2D/3D deformable registration for endoscopic camera images using self-supervised offline learning of intraoperative pneumothorax deformation
Shape registration of patient-specific organ shapes to endoscopic camera images is expected to be a key to realizing image-guided surgery, and a variety of applications of machine learning methods have been considered. Because the number of training data available from clinical cases is limited, the use of synthetic images generated from a statistical deformation model has been attempted; however, the influence on estimation caused by the difference between synthetic images and real scenes is a problem. In this study, we propose a self-supervised offline learning framework for model-based registration using image features commonly obtained from synthetic images and real camera images. Because of the limited number of endoscopic images available for training, we use a synthetic image generated from the nonlinear deformation model that represents possible intraoperative pneumothorax deformations. In order to solve the difficulty in estimating deformed shapes and viewpoints from the common image features obtained from synthetic and real images, we attempted to improve the registration error by adding the shading and distance information that can be obtained as prior knowledge in the synthetic image. Shape registration with real camera images is performed by learning the task of predicting the differential model parameters between two synthetic images. The developed framework achieved registration accuracy with a mean absolute error of less than 10 mm and a mean distance of less than 5 mm in a thoracoscopic pulmonary cancer resection, confirming improved prediction accuracy compared with conventional methods.
期刊介绍:
The purpose of the journal Computerized Medical Imaging and Graphics is to act as a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health. Included in the journal will be articles on novel computerized imaging or visualization techniques, including artificial intelligence and machine learning, augmented reality for surgical planning and guidance, big biomedical data visualization, computer-aided diagnosis, computerized-robotic surgery, image-guided therapy, imaging scanning and reconstruction, mobile and tele-imaging, radiomics, and imaging integration and modeling with other information relevant to digital health. The types of biomedical imaging include: magnetic resonance, computed tomography, ultrasound, nuclear medicine, X-ray, microwave, optical and multi-photon microscopy, video and sensory imaging, and the convergence of biomedical images with other non-imaging datasets.