Automated extraction of pleural effusion in three-dimensional thoracic CT images

Abstract

It is important for diagnosis of pulmonary diseases to measure volume of accumulating pleural effusion in threedimensional thoracic CT images quantitatively. However, automated extraction of pulmonary effusion correctly is difficult. Conventional extraction algorithm using a gray-level based threshold can not extract pleural effusion from thoracic wall or mediastinum correctly, because density of pleural effusion in CT images is similar to those of thoracic wall or mediastinum. So, we have developed an automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion. Our method used a template of lung obtained from a normal lung for segmentation of lungs with pleural effusions. Registration process consisted of two steps. First step was a global matching processing between normal and abnormal lungs of organs such as bronchi, bones (ribs, sternum and vertebrae) and upper surfaces of livers which were extracted using a region-growing algorithm. Second step was a local matching processing between normal and abnormal lungs which were deformed by the parameter obtained from the global matching processing. Finally, we segmented a lung with pleural effusion by use of the template which was deformed by two parameters obtained from the global matching processing and the local matching processing. We compared our method with a conventional extraction method using a gray-level based threshold and two published methods. The extraction rates of pleural effusions obtained from our method were much higher than those obtained from other methods. Automated extraction method of pulmonary effusion by use of extracting lung area with pleural effusion is promising for diagnosis of pulmonary diseases by providing quantitative volume of accumulating pleural effusion.

Multi-shape graph cuts with neighbor prior constraints and its application to lung segmentation from a chest CT volume

“…Further experiments with more cases of severe pathologies in the lung fields are also important work, which should be followed by improvements of the registration process for SSM of lungs and aorta by, for example, employing a simultaneous registration-segmentation scheme, and improvements of the shape energy, including proposal of a new shape energy that addresses shrinking bias problem (Vicente et al, 2008;Hanaoka et al, 2011). Comparison in performance of plural effusion extraction with other methods, such as registration based methods proposed by Sluimer et al (2008) and Kido and Tsunomori (2009) is another interesting future work. Finally we plan to apply the proposed multi-shape graph cuts with neighbor constraints to another segmentation problem, such as multi-organ segmentation from medical imagery.…”

“…In addition, it suffers from low accuracy in segmentation due to errors in registration and classification. Kido and Tsunomori (2009) proposed another registration based method using a template obtained from a normal case. Two step matching improved the performance of the case with severe plural effusion but still suffered from error in the registration.…”

Automatic Segmentation and Measurement of Pleural Effusions on CT

“…used cubic Hermite curve fitting and bounding box algorithms, using the central column of the image, and a Bessel method estimation of boundary points [16]. Kido and Tsunomori used a conditional region-growing algorithm, constrained by a template built through a global matching process between normal and abnormal lungs and surrounding landmarks [17]. …”