Kongkuo Lu

3D CT-Video Fusion for Image-Guided Bronchoscopy

Bronchoscopic biopsy of the central-chest lymph nodes is an important step for lung-cancer staging. Before bronchoscopy, the physician first visually assesses a patients three-dimensional (3D) computed tomography (CT) chest scan to identify suspect lymph-node sites. Next, during bronchoscopy, the physician guides the bronchoscope to each desired lymph-node site. Unfortunately, the physician has no link between the 3D CT image data and the live video stream provided during bronchoscopy. Thus, the physician must essentially perform biopsy blindly, and the skill levels between different physicians differ greatly. We describe an approach that enables synergistic fusion between the 3D CT data and the bronchoscopic video. Both the integrated planning and guidance system and the internal CT-video registration and fusion methods are described. Phantom, animal, and human studies illustrate the efficacy of the methods.

Segmentation of the central-chest lymph nodes in 3D MDCT images

Central-chest lymph nodes play a vital role in lung-cancer staging. The definition of lymph nodes from three-dimensional (3D) multidetector computed-tomography (MDCT) images, however, remains an open problem. We propose two methods for computer-based segmentation of the central-chest lymph nodes from a 3D MDCT scan: the single-section live wire and the single-click live wire. For the single-section live wire, the user first applies the standard live wire to a single two-dimensional (2D) section after which automated analysis completes the segmentation process. The single-click live wire is similar but is almost completely automatic. Ground-truth studies involving human 3D MDCT scans demonstrate the robustness, efficiency, and intra-observer and inter-observer reproducibility of the methods.

Extraction and visualization of the central chest lymph-node stations

Lung cancer remains the leading cause of cancer death in the United States and is expected to account for nearly 30% of all cancer deaths in 2007. Central to the lung-cancer diagnosis and staging process is the assessment of the central chest lymph nodes. This assessment typically requires two major stages: (1) location of the lymph nodes in a three-dimensional (3D) high-resolution volumetric multi-detector computed-tomography (MDCT) image of the chest; (2) subsequent nodal sampling using transbronchial needle aspiration (TBNA). We describe a computer-based system for automatically locating the central chest lymph-node stations in a 3D MDCT image. Automated analysis methods are first run that extract the airway tree, airway-tree centerlines, aorta, pulmonary artery, lungs, key skeletal structures, and major-airway labels. This information provides geometrical and anatomical cues for localizing the major nodal stations. Our system demarcates these stations, conforming to criteria outlined for the Mountain and Wang standard classification systems. Visualization tools within the system then enable the user to interact with these stations to locate visible lymph nodes. Results derived from a set of human 3D MDCT chest images illustrate the usage and efficacy of the system.

Automatic definition of the central-chest lymph-node stations

PurposeLung cancer remains the leading cause of cancer death in the United States. Central to the lung-cancer diagnosis and staging process is the assessment of the central-chest lymph nodes. This assessment requires two steps: (1) examination of the lymph-node stations and identification of diagnostically important nodes in a three-dimensional (3D) multidetector computed tomography (MDCT) chest scan; (2) tissue sampling of the identified nodes. We describe a computer-based system for automatically defining the central-chest lymph-node stations in a 3D MDCT chest scan.MethodsAutomated methods first construct a 3D chest model, consisting of the airway tree, aorta, pulmonary artery, and other anatomical structures. Subsequent automated analysis then defines the 3D regional nodal stations, as specified by the internationally standardized TNM lung-cancer staging system. This analysis involves extracting over 140 pertinent anatomical landmarks from structures contained in the 3D chest model. Next, the physician uses data mining tools within the system to interactively select diagnostically important lymph nodes contained in the regional nodal stations.ResultsResults from a ground-truth database of unlabeled lymph nodes identified in 32 MDCT scans verify the system’s performance. The system automatically defined 3D regional nodal stations that correctly labeled 96% of the database’s lymph nodes, with 93% of the stations correctly labeling 100% of their constituent nodes.ConclusionsThe system accurately defines the regional nodal stations in a given high-resolution 3D MDCT chest scan and eases a physician’s burden for analyzing a given MDCT scan for lymph-node station assessment. It also shows potential as an aid for preplanning lung-cancer staging procedures.

3D image fusion and guidance for computer-assisted bronchoscopy

The standard procedure for diagnosing lung cancer involves two stages. First, the physician evaluates a high-resolution three-dimensional (3D) computed-tomography (CT) chest image to produce a procedure plan. Next, the physician performs bronchoscopy on the patient, which involves navigating the the bronchoscope through the airways to planned biopsy sites. Unfortunately, the physician has no link between the 3D CT image data and the live video stream provided during bronchoscopy. In addition, these data sources differ greatly in what they physically give, and no true 3D planning tools exist for planning and guiding procedures. This makes it difficult for the physician to translate a CT-based procedure plan to the video domain of the bronchoscope. Thus, the physician must essentially perform biopsy blindly, and the skill levels between different physicians differ greatly. We describe a system that enables direct 3D CT-based procedure planning and provides direct 3D guidance during bronchoscopy. 3D CT-based information on biopsy sites is provided interactively as the physician moves the bronchoscope. Moreover, graphical information through a live fusion of the 3D CT data and bronchoscopic video is provided during the procedure. This information is coupled with a series of computer-graphics tools to give the physician a greatly augmented reality of the patients interior anatomy during a procedure. Through a series of controlled tests and studies with human lung-cancer patients, we have found that the system not only reduces the variation in skill level between different physicians, but also increases biopsy success rate.

Semi-automatic central-chest lymph-node definition from 3D MDCT images

Central-chest lymph nodes play a vital role in lung-cancer staging. The three-dimensional (3D) definition of lymph nodes from multidetector computed-tomography (MDCT) images, however, remains an open problem. This is because of the limitations in the MDCT imaging of soft-tissue structures and the complicated phenomena that influence the appearance of a lymph node in an MDCT image. In the past, we have made significant efforts toward developing (1) live-wire-based segmentation methods for defining 2D and 3D chest structures and (2) a computer-based system for automatic definition and interactive visualization of the Mountain central-chest lymph-node stations. Based on these works, we propose new single-click and single-section live-wire methods for segmenting central-chest lymph nodes. The single-click live wire only requires the user to select an object pixel on one 2D MDCT section and is designed for typical lymph nodes. The single-section live wire requires the user to process one selected 2D section using standard 2D live wire, but it is more robust. We applied these methods to the segmentation of 20 lymph nodes from two human MDCT chest scans (10 per scan) drawn from our ground-truth database. The single-click live wire segmented 75% of the selected nodes successfully and reproducibly, while the success rate for the single-section live wire was 85%. We are able to segment the remaining nodes, using our previously derived (but more interaction intense) 2D live-wire method incorporated in our lymph-node analysis system. Both proposed methods are reliable and applicable to a wide range of pulmonary lymph nodes.

Quantitative analysis of the central-chest lymph nodes based on 3D MDCT image data

Lung cancer is the leading cause of cancer death in the United States. In lung-cancer staging, central-chest lymph nodes and associated nodal stations, as observed in three-dimensional (3D) multidetector CT (MDCT) scans, play a vital role. However, little work has been done in relation to lymph nodes, based on MDCT data, due to the complicated phenomena that give rise to them. Using our custom computer-based system for 3D MDCT-based pulmonary lymph-node analysis, we conduct a detailed study of lymph nodes as depicted in 3D MDCT scans. In this work, the Mountain lymph-node stations are automatically defined by the system. These defined stations, in conjunction with our systems image processing and visualization tools, facilitate lymph-node detection, classification, and segmentation. An expert pulmonologist, chest radiologist, and trained technician verified the accuracy of the automatically defined stations and indicated observable lymph nodes. Next, using semi-automatic tools in our system, we defined all indicated nodes. Finally, we performed a global quantitative analysis of the characteristics of the observed nodes and stations. This study drew upon a database of 32 human MDCT chest scans. 320 Mountain-based stations (10 per scan) and 852 pulmonary lymph nodes were defined overall from this database. Based on the numerical results, over 90% of the automatically defined stations were deemed accurate. This paper also presents a detailed summary of central-chest lymph-node characteristics for the first time.