Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

Dustin Sargent; Sun Young Park

doi:10.1117/12.2254575

24 February 2017 Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

Dustin Sargent, Sun Young Park

Proceedings Volume 10133, Medical Imaging 2017: Image Processing; 101332R (2017) https://doi.org/10.1117/12.2254575
Event: SPIE Medical Imaging, 2017, Orlando, Florida, United States

Abstract

We present a method for semi-automatic segmentation of lung nodules in chest CT that can be extended to general lesion segmentation in multiple modalities. Most semi-automatic algorithms for lesion segmentation or similar tasks use region-growing or edge-based contour finding methods such as level-set. However, lung nodules and other lesions are often connected to surrounding tissues, which makes these algorithms prone to growing the nodule boundary into the surrounding tissue. To solve this problem, we apply a 3D extension of the 2D edge linking method with dynamic programming to find a closed surface in a spherical representation of the nodule ROI. The algorithm requires a user to draw a maximal diameter across the nodule in the slice in which the nodule cross section is the largest. We report the lesion volume estimation accuracy of our algorithm on the FDA lung phantom dataset, and the RECIST diameter estimation accuracy on the lung nodule dataset from the SPIE 2016 lung nodule classification challenge. The phantom results in particular demonstrate that our algorithm has the potential to mitigate the disparity in measurements performed by different radiologists on the same lesions, which could improve the accuracy of disease progression tracking.

Citation Download Citation

Dustin Sargent and Sun Young Park "Semi-automatic 3D lung nodule segmentation in CT using dynamic programming", Proc. SPIE 10133, Medical Imaging 2017: Image Processing, 101332R (24 February 2017); https://doi.org/10.1117/12.2254575

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