Automatic segmentation of tumor-laden lung volumes from the LIDC database

Walter G. O'Dell

doi:10.1117/12.911379

23 February 2012 Automatic segmentation of tumor-laden lung volumes from the LIDC database

Walter G. O'Dell

Proceedings Volume 8315, Medical Imaging 2012: Computer-Aided Diagnosis; 831531 (2012) https://doi.org/10.1117/12.911379
Event: SPIE Medical Imaging, 2012, San Diego, California, United States

Abstract

The segmentation of the lung parenchyma is often a critical pre-processing step prior to application of computer-aided detection of lung nodules. Segmentation of the lung volume can dramatically decrease computation time and reduce the number of false positive detections by excluding from consideration extra-pulmonary tissue. However, while many algorithms are capable of adequately segmenting the healthy lung, none have been demonstrated to work reliably well on tumor-laden lungs. Of particular challenge is to preserve tumorous masses attached to the chest wall, mediastinum or major vessels. In this role, lung volume segmentation comprises an important computational step that can adversely affect the performance of the overall CAD algorithm. An automated lung volume segmentation algorithm has been developed with the goals to maximally exclude extra-pulmonary tissue while retaining all true nodules. The algorithm comprises a series of tasks including intensity thresholding, 2-D and 3-D morphological operations, 2-D and 3-D floodfilling, and snake-based clipping of nodules attached to the chest wall. It features the ability to (1) exclude trachea and bowels, (2) snip large attached nodules using snakes, (3) snip small attached nodules using dilation, (4) preserve large masses fully internal to lung volume, (5) account for basal aspects of the lung where in a 2-D slice the lower sections appear to be disconnected from main lung, and (6) achieve separation of the right and left hemi-lungs. The algorithm was developed and trained to on the first 100 datasets of the LIDC image database.

Citation Download Citation

Walter G. O'Dell "Automatic segmentation of tumor-laden lung volumes from the LIDC database", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 831531 (23 February 2012); https://doi.org/10.1117/12.911379

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