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23 February 2012 A minimally interactive method to segment enlarged lymph nodes in 3D thoracic CT images using a rotatable spiral-scanning technique
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Abstract
Precise size measurement of enlarged lymph nodes is a significant indicator for diagnosing malignancy, follow-up and therapy monitoring of cancer diseases. The presence of diverse sizes and shapes, inhomogeneous enhancement and the adjacency to neighboring structures with similar intensities, make the segmentation task challenging. We present a semi-automatic approach requiring minimal user interactions to fast and robustly segment the enlarged lymph nodes. First, a stroke approximating the largest diameter of a specific lymph node is drawn manually from which a volume of interest (VOI) is determined. Second, Based on the statistical analysis of the intensities on the dilated stroke area, a region growing procedure is utilized within the VOI to create an initial segmentation of the target lymph node. Third, a rotatable spiral-scanning technique is proposed to resample the 3D boundary surface of the lymph node to a 2D boundary contour in a transformed polar image. The boundary contour is found by seeking the optimal path in 2D polar image with dynamic programming algorithm and eventually transformed back to 3D. Ultimately, the boundary surface of the lymph node is determined using an interpolation scheme followed by post-processing steps. To test the robustness and efficiency of our method, a quantitative evaluation was conducted with a dataset of 315 lymph nodes acquired from 79 patients with lymphoma and melanoma. Compared to the reference segmentations, an average Dice coefficient of 0.88 with a standard deviation of 0.08, and an average absolute surface distance of 0.54mm with a standard deviation of 0.48mm, were achieved.
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Lei Wang, Jan Hendrik Moltz, Lars Bornemann, and Horst K. Hahn "A minimally interactive method to segment enlarged lymph nodes in 3D thoracic CT images using a rotatable spiral-scanning technique", Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis, 83150D (23 February 2012); https://doi.org/10.1117/12.912935
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