Region-based snake with edge constraint for segmentation of lymph nodes on CT images

Lymph nodes segmentation is a tedious process with large inter-user variability when performed manually. To facilitate lymph nodes assessment for lung cancer patient, we present an automatic and improved snake segmentation method for thoracic lymph nodes on CT images in this paper. We first investigated the performance of both edge-based and region-based snake algorithms for the segmentation task, using a B-spline contour parameterization. The effect of the number of B-spline control points on the snake performance was also examined. Both edge-based and region-based snakes were found to have their own advantages and disadvantages for lymph nodes segmentation. We further developed a method of region-based snake with edge constraint, which utilizes a self-adjusting mechanism to integrate both edge and region information in a constructive manner. The average Dice Similarity Coefficient obtained was 0.853 ± 0.059 and 0.841 ± 0.108 for the baseline and follow-up lymph nodes respectively using the proposed method. The method was found to be an effective lymph node segmentation method and would potentially be useful to help with treatment response evaluations in the clinical practice.

[1]  Xose Manuel Pardo,et al.  A snake for CT image segmentation integrating region and edge information , 2001, Image Vis. Comput..

[2]  Marius Erdt,et al.  Lymph node segmentation in CT slices using dynamic programming , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[3]  Klaus D. Tönnies,et al.  Stable dynamic 3D shape models , 2005, IEEE International Conference on Image Processing 2005.

[4]  Wesley E. Snyder,et al.  Three-dimensional active surface approach to lymph node segmentation , 1999, Medical Imaging.

[5]  E F Halpern,et al.  Evaluation of selected two-dimensional segmentation techniques for computed tomography quantitation of lymph nodes. , 1996, Investigative radiology.

[6]  Rachid Deriche,et al.  Unifying boundary and region-based information for geodesic active tracking , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[7]  Anant Madabhushi,et al.  An Integrated Region-, Boundary-, Shape-Based Active Contour for Multiple Object Overlap Resolution in Histological Imagery , 2012, IEEE Transactions on Medical Imaging.

[8]  Xavier Cufí,et al.  Strategies for image segmentation combining region and boundary information , 2003, Pattern Recognit. Lett..

[9]  Heinz-Otto Peitgen,et al.  Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans , 2009, IEEE Journal of Selected Topics in Signal Processing.

[10]  L. Schwartz,et al.  New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). , 2009, European journal of cancer.

[11]  Bernhard Preim,et al.  Segmentation of neck lymph nodes in CT datasets with stable 3D mass-spring models segmentation of neck lymph nodes. , 2007, Academic radiology.

[12]  Daniel Rueckert,et al.  Nonrigid registration using free-form deformations: application to breast MR images , 1999, IEEE Transactions on Medical Imaging.

[13]  Wesley E. Snyder,et al.  Lymph node segmentation using active contours , 1997, Medical Imaging.

[14]  Jana Dornheim,et al.  Complete fully automatic model-based segmentation of normal and pathological lymph nodes in CT data , 2010, International Journal of Computer Assisted Radiology and Surgery.

[15]  Tony F. Chan,et al.  Active contours without edges , 2001, IEEE Trans. Image Process..

[16]  Bernhard Preim,et al.  Segmentation of Neck Lymph Nodes in CT Datasets with Stable 3D Mass-Spring Models , 2006, MICCAI.

[17]  Marius Erdt,et al.  Lymph node segmentation in CT images using a size invariant Mass Spring Model , 2010, Proceedings of the 10th IEEE International Conference on Information Technology and Applications in Biomedicine.

[18]  Binsheng Zhao,et al.  Marker-controlled watershed for lymphoma segmentation in sequential CT images. , 2006, Medical physics.

[19]  L. Schwartz,et al.  Lymph node segmentation from CT images using fast marching method. , 2004, Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society.