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.