Depth-based branching level estimation for bronchoscopic navigation.

PURPOSE: Bronchoscopists rely on navigation systems during bronchoscopy to reduce the risk of getting lost in the complex bronchial tree-like structure and the homogeneous bronchus lumens. We propose a patient-specific branching level estimation method for bronchoscopic navigation because it is vital to identify the branches being examined in the bronchus tree during examination. METHODS: We estimate the branching level by integrating the changes in the number of bronchial orifices and the camera motions among the frames. We extract the bronchial orifice regions from a depth image, which is generated using a cycle generative adversarial network (CycleGAN) from real bronchoscopic images. We calculate the number of orifice regions using the vertical and horizontal projection profiles of the depth images and obtain the camera-moving direction using the feature point-based camera motion estimation. The changes in the number of bronchial orifices are combined with the camera-moving direction to estimate the branching level. RESULTS: We used three in vivo and one phantom case to train the CycleGAN model and four in vivo cases to validate the proposed method. We manually created the ground truth of the branching level. The experimental results showed that the proposed method can estimate the branching level with an average accuracy of 87.6%. The processing time per frame was about 61 ms. CONCLUSION: Experimental results show that it is feasible to estimate the branching level using the number of bronchial orifices and camera-motion estimation from real bronchoscopic images.

A visual SLAM-based bronchoscope tracking scheme for bronchoscopic navigation.

PURPOSE: Due to the complex anatomical structure of bronchi and the resembling inner surfaces of airway lumina, bronchoscopic examinations require additional 3D navigational information to assist the physicians. A bronchoscopic navigation system provides the position of the endoscope in CT images with augmented anatomical information. To overcome the shortcomings of previous navigation systems, we propose using a technique known as visual simultaneous localization and mapping (SLAM) to improve bronchoscope tracking in navigation systems. METHODS: We propose an improved version of the visual SLAM algorithm and use it to estimate nt-specific bronchoscopic video as input. We improve the tracking procedure by adding more narrow criteria in feature matching to avoid mismatches. For validation, we collected several trials of bronchoscopic videos with a bronchoscope camera by exploring synthetic rubber bronchus phantoms. We simulated breath by adding periodic force to deform the phantom. We compared the camera positions from visual SLAM with the manually created ground truth of the camera pose. The number of successfully tracked frames was also compared between the original SLAM and the proposed method. RESULTS: We successfully tracked 29,559 frames at a speed of 80 ms per frame. This corresponds to 78.1% of all acquired frames. The average root mean square error for our technique was 3.02 mm, while that for the original was 3.61 mm. CONCLUSION: We present a novel methodology using visual SLAM for bronchoscope tracking. Our experimental results showed that it is feasible to use visual SLAM for the estimation of the bronchoscope camera pose during bronchoscopic navigation. Our proposed method tracked more frames and showed higher accuracy than the original technique did. Future work will include combining the tracking results with virtual bronchoscopy and validation with in vivo cases.

Three Cases of Ampullary Neuroendocrine Tumor Treated by Endoscopic Papillectomy: A Case Report and Literature Review.

We herein report three cases of patients with an ampullary neuroendocrine tumor (NET), who underwent endoscopic papillectomy (EP). No tumor recurrence or metastasis was detected in the patients for more than two years after EP. Generally, surgical resection is recommended for ampullary NETs by the European Neuroendocrine Tumor Society. However, as EP is less invasive than surgical resection, there are some reports of low-grade small ampullary NETs curatively treated by EP with long-term follow-up. We consider that EP may be a curative treatment for small and low-grade ampullary NETs without regional or distant metastasis.

Assessment of COPD severity by combining pulmonary function tests and chest CT images.

PURPOSE: Chronic obstructive pulmonary disease (COPD) is characterized by airflow limitations. Physicians frequently assess the stage using pulmonary function tests and chest CT images. This paper describes a novel method to assess COPD severity by combining measurements of pulmonary function tests (PFT) and the results of chest CT image analysis. METHODS: The proposed method utilizes measurements from PFTs and chest CT scans to assess COPD severity. This method automatically classifies COPD severity into five stages, described in GOLD guidelines, by a multi-class AdaBoost classifier. The classifier utilizes 24 measurements as feature values, which include 18 measurements from PFTs and six measurements based on chest CT image analysis. A total of 3 normal and 46 abnormal (COPD) examinations performed in adults were evaluated using the proposed method to test its diagnostic capability. RESULTS: The experimental results revealed that its accuracy rates were 100.0 % (resubstitution scheme) and 53.1 % (leave-one-out scheme). A total of 95.7 % of missed classifications were assigned in the neighboring severities. CONCLUSIONS: These results demonstrate that the proposed method is a feasible means to assess COPD severity. A much larger sample size will be required to establish the limits of the method and provide clinical validation.

Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images.

PURPOSE: Pulmonary nodules may indicate the early stage of lung cancer, and the progress of lung cancer causes associated changes in the shape and number of pulmonary blood vessels. The automatic segmentation of pulmonary nodules and blood vessels is desirable for chest computer-aided diagnosis (CAD) systems. Since pulmonary nodules and blood vessels are often attached to each other, conventional nodule detection methods usually produce many false positives (FPs) in the blood vessel regions, and blood vessel segmentation methods may incorrectly segment the nodules that are attached to the blood vessels. A method to simultaneously and separately segment the pulmonary nodules and blood vessels was developed and tested. METHOD: A line structure enhancement (LSE) filter and a blob-like structure enhancement (BSE) filter were used to augment initial selection of vessel regions and nodule candidates, respectively. A front surface propagation (FSP) procedure was employed for precise segmentation of blood vessels and nodules. By employing a speed function that becomes fast at the initial vessel regions and slow at the nodule candidates to propagate the front surface, the front surface can be propagated to cover the blood vessel region with suppressed nodules. Hence, the resultant region covered by the front surface indicates pulmonary blood vessels. The lung nodule regions were finally obtained by removing the nodule candidates that are covered by the front surface. RESULT: A test data set was assembled including 20 standard-dose chest CT images obtained from a local database and 20 low-dose chest CT images obtained from lung image database consortium (LIDC). The average extraction rate of the pulmonary blood vessels was about 93%. The average TP rate of nodule detection was 95% with 9.8 FPs/case in standard-dose CT image, and 91.5% with 10.5 FPs/case in low-dose CT image, respectively. CONCLUSION: Pulmonary blood vessels and nodules segmentation method based on local intensity structure analysis and front surface propagation were developed. The method was shown to be feasible for nodule detection and vessel extraction in chest CAD.