By providing an open-source platform for research investigations, we believe that novel and collaborative approaches can overcome the limitations of current EMT technology.

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Peripheral lung nodules remain challenging for accurate localization and diagnosis. Once identified, there are many strategies for diagnosis with heterogeneous risk benefit analysis. Traditional strategies such as conventional bronchoscopy have poor performance in locating and acquiring the required tissue. Similarly, while computerized-assisted transthoracic needle biopsy is currently the favored diagnostic procedure, it is associated with complications such as pneumothorax and hemorrhage. Video-assisted thoracoscopic and open surgical biopsies are invasive, require general anesthesia and are therefore not a first-line approach. New techniques such as ultrathin bronchoscopy and image-based guidance technologies are evolving to improve the diagnosis of peripheral lung lesions. Virtual bronchoscopy and electromagnetic navigation systems are novel technologies based on assisted-computerized tomography images that guide the bronchoscopist toward the target peripheral lesion. This article provides a comprehensive review of these emerging technologies.

Background Computed tomography (CT) helps physicians locate and diagnose pathological conditions. In some conditions, having an airway segmentation method which facilitates reconstruction of the airway from chest CT images can help hugely in the assessment of lung diseases. Many efforts have been made to develop airway segmentation algorithms, but methods are usually not optimized to be reliable across different CT scan parameters.MethodsIn this paper, we present a simple and reliable semi-automatic algorithm which can segment tracheal and bronchial anatomy using the open-source 3D Slicer platform. The method is based on a region growing approach where trachea, right and left bronchi are cropped and segmented independently using three different thresholds. The algorithm and its parameters have been optimized to be efficient across different CT scan acquisition parameters. The performance of the proposed method has been evaluated on EXACT’09 cases and local clinical cases as well as on a breathing pig lung phantom using multiple scans and changing parameters. In particular, to investigate multiple scan parameters reconstruction kernel, radiation dose and slice thickness have been considered. Volume, branch count, branch length and leakage presence have been evaluated. A new method for leakage evaluation has been developed and correlation between segmentation metrics and CT acquisition parameters has been considered.ResultsAll the considered cases have been segmented successfully with good results in terms of leakage presence. Results on clinical data are comparable to other teams’ methods, as obtained by evaluation against the EXACT09 challenge, whereas results obtained from the phantom prove the reliability of the method across multiple CT platforms and acquisition parameters. As expected, slice thickness is the parameter affecting the results the most, whereas reconstruction kernel and radiation dose seem not to particularly affect airway segmentation.ConclusionThe system represents the first open-source airway segmentation platform. The quantitative evaluation approach presented represents the first repeatable system evaluation tool for like-for-like comparison between different airway segmentation platforms. Results suggest that the algorithm can be considered stable across multiple CT platforms and acquisition parameters and can be considered as a starting point for the development of a complete airway segmentation algorithm.