RESUMEN
PURPOSE: Computed tomography (CT) imaging is the modality of choice for lung cancer diagnostics. With the increasing number of lung interventions on sublobar level in recent years, determining and visualizing pulmonary segments in CT images and, in oncological cases, reliable segment-related information about the location of tumors has become increasingly desirable. Computer-assisted identification of lung segments in CT images is subject of this work. METHODS: The authors present a new interactive approach for the segmentation of lung segments that uses the Euclidean distance of each point in the lung to the segmental branches of the pulmonary artery. The aim is to analyze the potential of the method. Detailed manual pulmonary artery segmentations are used to achieve the best possible segment approximation results. A detailed description of the method and its evaluation on 11 CT scans from clinical routine are given. RESULTS: An accuracy of 2-3 mm is measured for the segment boundaries computed by the pulmonary artery-based method. On average, maximum deviations of 8 mm are observed. 135 intersegmental pulmonary veins detected in the 11 test CT scans serve as reference data. Furthermore, a comparison of the presented pulmonary artery-based approach to a similar approach that uses the Euclidean distance to the segmental branches of the bronchial tree is presented. It shows a significantly higher accuracy for the pulmonary artery-based approach in lung regions at least 30 mm distal to the lung hilum. CONCLUSIONS: A pulmonary artery-based determination of lung segments in CT images is promising. In the tests, the pulmonary artery-based determination has been shown to be superior to the bronchial tree-based determination. The suitability of the segment approximation method for application in the planning of segment resections in clinical practice has already been verified in experimental cases. However, automation of the method accompanied by an evaluation on a larger number of test cases is required before application in the daily clinical routine.
Asunto(s)
Procesamiento de Imagen Asistido por Computador/métodos , Pulmón/irrigación sanguínea , Pulmón/diagnóstico por imagen , Arteria Pulmonar/diagnóstico por imagen , Tomografía Computarizada por Rayos X/métodos , AlgoritmosRESUMEN
Segmentation of the pulmonary lobes is relevant in clinical practice and particularly challenging for cases with severe diseases or incomplete fissures. In this work, an automated segmentation approach is presented that performs a marker-based watershed transformation on computed tomography (CT) scans to subdivide the lungs into lobes. A cost image for the watershed transformation is computed by combining information from fissures, bronchi, and pulmonary vessels. The lobar markers are calculated by an analysis of the automatically labeled bronchial tree. By integration of information from several anatomical structures the segmentation is made robust against incomplete fissures. For evaluation the method was compared to a recently published method on 20 CT scans with no or mild disease. The average distances to the reference segmentation were 0.69, 0.67, and 1.21 mm for the left major, right major, and right minor fissure, respectively. In addition the results were submitted to LOLA11, an international lung lobe segmentation challenge with publically available data including cases with severe diseases. The average distances to the reference for the 55 CT scans provided by LOLA11 were 0.98, 3.97, and 3.09 mm for the left major, right major, and right minor fissure. Moreover, an analysis of the relation between segmentation quality and fissure completeness showed that the method is robust against incomplete fissures.