Yongqiang Tan1, Lin Lu2, Apurva Bonde3, Deling Wang4, Jing Qi5, Lawrence H Schwartz2, Binsheng Zhao2. 1. Google Inc, Los Angeles, CA, 90291, USA. 2. Department of Radiology, Columbia University Medical Center, New York, NY, 10032, USA. 3. Department of Radiology, Oregon Health and Science University, Portland, OR, 97239, USA. 4. Medical imaging and minimally invasive interventional center, Sun Yat-sen university cancer center, Guangzhou, 510060, China. 5. Department of Radiology, Children's Hospital of Wisconsin, Wauwatosa, WI, 53226, USA.
Abstract
PURPOSE: Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. METHODS: The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. RESULTS: The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. CONCLUSIONS: The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes.
PURPOSE: Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. METHODS: The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. RESULTS: The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. CONCLUSIONS: The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes.