Int J Comput Assist Radiol Surg - Automatic segmentation of pulmonary blood vessels and nodules based on local intensity structure analysis and surface propagation in 3D chest CT images.

Tópicos

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Resumo

RPOSE: Pulmonary nodules may indicate the early stage of lung cancer, and the progress of lung cancer causes associated changes in the shape and number of pulmonary blood vessels. The automatic segmentation of pulmonary nodules and blood vessels is desirable for chest computer-aided diagnosis (CAD) systems. Since pulmonary nodules and blood vessels are often attached to each other, conventional nodule detection methods usually produce many false positives (FPs) in the blood vessel regions, and blood vessel segmentation methods may incorrectly segment the nodules that are attached to the blood vessels. A method to simultaneously and separately segment the pulmonary nodules and blood vessels was developed and tested.METHOD: A line structure enhancement (LSE) filter and a blob-like structure enhancement (BSE) filter were used to augment initial selection of vessel regions and nodule candidates, respectively. A front surface propagation (FSP) procedure was employed for precise segmentation of blood vessels and nodules. By employing a speed function that becomes fast at the initial vessel regions and slow at the nodule candidates to propagate the front surface, the front surface can be propagated to cover the blood vessel region with suppressed nodules. Hence, the resultant region covered by the front surface indicates pulmonary blood vessels. The lung nodule regions were finally obtained by removing the nodule candidates that are covered by the front surface.RESULT: A test data set was assembled including 20 standard-dose chest CT images obtained from a local database and 20 low-dose chest CT images obtained from lung image database consortium (LIDC). The average extraction rate of the pulmonary blood vessels was about 93%. The average TP rate of nodule detection was 95% with 9.8 FPs/case in standard-dose CT image, and 91.5% with 10.5 FPs/case in low-dose CT image, respectively.CONCLUSION: Pulmonary blood vessels and nodules segmentation method based on local intensity structure analysis and front surface propagation were developed. The method was shown to be feasible for nodule detection and vessel extraction in chest CAD.

Resumo Limpo

rpose pulmonari nodul may indic earli stage lung cancer progress lung cancer caus associ chang shape number pulmonari blood vessel automat segment pulmonari nodul blood vessel desir chest computeraid diagnosi cad system sinc pulmonari nodul blood vessel often attach convent nodul detect method usual produc mani fals posit fps blood vessel region blood vessel segment method may incorrect segment nodul attach blood vessel method simultan separ segment pulmonari nodul blood vessel develop testedmethod line structur enhanc lse filter bloblik structur enhanc bse filter use augment initi select vessel region nodul candid respect front surfac propag fsp procedur employ precis segment blood vessel nodul employ speed function becom fast initi vessel region slow nodul candid propag front surfac front surfac can propag cover blood vessel region suppress nodul henc result region cover front surfac indic pulmonari blood vessel lung nodul region final obtain remov nodul candid cover front surfaceresult test data set assembl includ standarddos chest ct imag obtain local databas lowdos chest ct imag obtain lung imag databas consortium lidc averag extract rate pulmonari blood vessel averag tp rate nodul detect fpscase standarddos ct imag fpscase lowdos ct imag respectivelyconclus pulmonari blood vessel nodul segment method base local intens structur analysi front surfac propag develop method shown feasibl nodul detect vessel extract chest cad

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