Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate

Artit C Jirapatnakul,Claudia I Henschke,Anthony P Reeves,Yury D Mulman,David F Yankelevitz

doi:10.1155/2011/632195

Artit C Jirapatnakul, Claudia I Henschke + Show 3 more

Open Access

https://doi.org/10.1155/2011/632195

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Abstract

An algorithm was developed to segment solid pulmonary nodules attached to the chest wall in computed tomography scans. The pleural surface was estimated and used to segment the nodule from the chest wall. To estimate the surface, a robust approach was used to identify points that lie on the pleural surface but not on the nodule. A 3D surface was estimated from the identified surface points. The segmentation performance of the algorithm was evaluated on a database of 150 solid juxtapleural pulmonary nodules. Segmented images were rated on a scale of 1 to 4 based on visual inspection, with 3 and 4 considered acceptable. This algorithm offers a large improvement in the success rate of juxtapleural nodule segmentation, successfully segmenting 98.0% of nodules compared to 81.3% for a previously published plane-fitting algorithm, which will provide for the development of more robust automated nodule measurement methods.

Highlights

One of the most reliable indicators of the malignancy of a pulmonary nodule is its growth rate [1, 2]
Juxtapleural pulmonary nodules are attached to the chest wall and pleural surface; these nodules present a challenge to many automated measurement algorithms due to the need to decide on a boundary between the nodule and chest wall without the presence of any difference in intensity between the two structures
The modeled pleural surface (MPS) accurately describes the location of the pleural surface inside the high voxel intensity region of the thoracic wall (TW) based on the cumulative pleural surface evidence provided by the visible surface

Summary

Introduction

One of the most reliable indicators of the malignancy of a pulmonary nodule is its growth rate [1, 2]. Note that the nodule is the same intensity as the lung wall, rendering intensity-based segmentation methods ineffective with juxtapleural nodules—instead, a decision for locating the nodule boundary can only be based on properties of adjacent sections of the pleural surface Previous works in this area have segmented juxtapleural nodules using morphological filtering or various surface-fitting algorithms. Another approach by Okada et al [7] used robust anisotropic Gaussian fitting followed by a morphological opening operation; their method was able to achieve 94.8% correct segmentation on a dataset of 1312 nodules, both attached and isolated Other research on this topic includes a study by Shen et al [8] which used a surface smoothing

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