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

IF 5.4 2区 医学 Q1 ENGINEERING, BIOMEDICAL
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引用次数: 0

摘要

将患者特定器官形状与内窥镜相机图像进行形状配准有望成为实现图像引导手术的关键,机器学习方法的各种应用已被考虑。由于从临床病例中获得的训练数据数量有限,人们尝试使用由统计变形模型生成的合成图像,但合成图像与真实场景之间的差异对估计的影响是一个问题。在本研究中,我们提出了一种自监督离线学习框架,利用从合成图像和真实摄像机图像中获得的图像特征,进行基于模型的配准。由于可用于训练的内窥镜图像数量有限,我们使用了由非线性变形模型生成的合成图像,该模型代表了术中可能出现的气胸变形。为了解决从合成图像和真实图像获得的共同图像特征中估计变形形状和视点的困难,我们尝试通过添加阴影和距离信息来改善配准误差,这些信息可以作为先验知识在合成图像中获得。通过学习预测两幅合成图像之间的差分模型参数的任务,实现与真实相机图像的形状配准。在胸腔镜肺癌切除术中,所开发的框架达到了平均绝对误差小于 10 毫米、平均距离小于 5 毫米的配准精度,证实了与传统方法相比预测精度的提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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来源期刊
CiteScore
10.70
自引率
3.50%
发文量
71
审稿时长
26 days
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.
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