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arXiv 1806.03014cs.CV
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一种基于机器学习的结肠镜跟踪结肠变形估计方法

Machine learning-based colon deformation estimation method for colonoscope tracking

Graduate School of Informatics, Nagoya University（名古屋大学信息学院研究生院）；School of Information Science, Aichi Institute of Technology（爱知工业大学信息科学学院）；Department of Endoscopy, Nagoya University Hospital（名古屋大学医院内镜科）；Dept. of Gastro. and Hepato., Nagoya University Graduate School of Medicine（名古屋大学医学院胃肠肝病学系）；Technical University of Munich（慕尼黑工业大学）

Masahiro Oda, Takayuki Kitasaka, Kazuhiro Furukawa, Ryoji Miyahara, Yoshiki Hirooka, Hidemi Goto, Nassir Navab, Kensaku Mori

备注：
Accepted paper for oral presentation at SPIE Medical Imaging 2018, Houston, TX, USA
发表信息：
SPIE Medical Imaging 2018: Image-Guided Procedures, Robotic Interventions, and Modeling
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摘要：

本文提出了一种结肠变形估计方法，可用于估计结肠镜插入过程中的结肠变形。需要在结肠镜插入期间将医生导航到息肉位置的结肠镜跟踪或导航系统，以减少诸如结肠穿孔的并发症。先前的结肠镜跟踪方法通过配准结肠镜形状和结肠形状来获得结肠中的结肠镜位置。使用电磁传感器获得结肠镜形状，并且从CT体积获得结肠形状。然而，由于结肠镜插入过程中发生的结肠变形，观察到较大的跟踪误差。这种变形使得配准困难。由于结肠变形是由结肠镜引起的，因此结肠变形与结肠镜形状之间存在很强的关系。为了减少跟踪误差，需要一种结肠镜插入过程中结肠变形的估计方法。我们提出了一种结肠变形估计方法。该方法用于从结肠镜形状估计变形的结肠形状。我们使用回归森林算法来估计变形的结肠形状。回归森林算法使用结肠和结肠镜形状对进行训练，其中包含结肠镜插入期间发生的变形。作为初步的研究，我们利用该方法来估计结肠体模的变形。在我们的实验中，所提出的方法正确地估计变形结肠体模形状。

英文摘要：

This paper presents a colon deformation estimation method, which can be used to estimate colon deformations during colonoscope insertions. Colonoscope tracking or navigation system that navigates a physician to polyp positions during a colonoscope insertion is required to reduce complications such as colon perforation. A previous colonoscope tracking method obtains a colonoscope position in the colon by registering a colonoscope shape and a colon shape. The colonoscope shape is obtained using an electromagnetic sensor, and the colon shape is obtained from a CT volume. However, large tracking errors were observed due to colon deformations occurred during colonoscope insertions. Such deformations make the registration difficult. Because the colon deformation is caused by a colonoscope, there is a strong relationship between the colon deformation and the colonoscope shape. An estimation method of colon deformations occur during colonoscope insertions is necessary to reduce tracking errors. We propose a colon deformation estimation method. This method is used to estimate a deformed colon shape from a colonoscope shape. We use the regression forests algorithm to estimate a deformed colon shape. The regression forests algorithm is trained using pairs of colon and colonoscope shapes, which contains deformations occur during colonoscope insertions. As a preliminary study, we utilized the method to estimate deformations of a colon phantom. In our experiments, the proposed method correctly estimated deformed colon phantom shapes.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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