Automated anatomical labeling of bronchial branches extracted from CT datasets based on machine learning and combination optimization and its application to bronchoscope guidance.

This paper presents a method for the automated anatomical labeling of bronchial branches extracted from 3D CT images based on machine learning and combination optimization. We also show applications of anatomical labeling on a bronchoscopy guidance system. This paper performs automated labeling by using machine learning and combination optimization. The actual procedure consists of four steps: (a) extraction of tree structures of the bronchus regions extracted from CT images, (b) construction of AdaBoost classifiers, (c) computation of candidate names for all branches by using the classifiers, (d) selection of best combination of anatomical names. We applied the proposed method to 90 cases of 3D CT datasets. The experimental results showed that the proposed method can assign correct anatomical names to 86.9% of the bronchial branches up to the sub-segmental lobe branches. Also, we overlaid the anatomical names of bronchial branches on real bronchoscopic views to guide real bronchoscopy.

Improvement of accuracy of marker-free bronchoscope tracking using electromagnetic tracker based on bronchial branch information.

This paper presents a study of tracking accuracy improvement of marker-free bronchoscope tracking using an electromagnetic tracking system. Bronchoscope tracking is an important function in a bronchoscope navigation system that assists a physician during bronchoscopic examination. Several research groups have presented a method for bronchoscope tracking using an ultra-tiny electromagnetic tracker (UEMT) that can be inserted into the working channel of a bronchoscope. In such a system, it is necessary to find the matrix T showing the relation between the coordinate systems of the CT image and the UEMT. This paper tries to improve the accuracy of this matrix by using not only the position information of the UEMT but also the orientation information. The proposed algorithm uses the running direction information of bronchial branches and the orientation information of the UEMT in the computation process of T. In the experiments using a bronchial phantom, the tracking accuracy was improved from 2.2 mm to 1.8 mm.

Bronchoscope tracking without fiducial markers using ultra-tiny electromagnetic tracking system and its evaluation in different environments.

This paper presents a method for bronchoscope tracking without any fiducial markers using an ultra-tiny electromagnetic tracker (UEMT) for a bronchoscopy guidance system. The proposed method calculates the transformation matrix, which shows the relationship between the coordinates systems of the pre-operative CT images and the UEMT, by registering bronchial branches segmented from CT images and points measured by the UEMT attached at the tip of a bronchoscope. We dynamically compute the transformation matrix for every pre-defined number of measurements. We applied the proposed method to a bronchial phantom in several experimental environments. The experimental results showed the proposed method can track a bronchoscope camera with about 3.3mm of target registration error (TRE) for wood table environment and 4.0mm of TRE for examination table environment.