International journal of computer assisted radiology and surgery · 2011
Automatic definition of the central-chest lymph-node stations.
Сравнительное исследованиеСтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод в очереди
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Lu Kongkuo, Taeprasartsit Pinyo, Bascom Rebecca, Mahraj Rickhesvar P M, Higgins William E
Аннотация
PURPOSE: Lung 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.
METHODS: Automated 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.
RESULTS: Results 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.
CONCLUSIONS: The 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.
METHODS: Automated 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.
RESULTS: Results 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.
CONCLUSIONS: The 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.