The feasibility of hybrid automatic segmentation of axillary lymph nodes from a 3-D sonogram.
The use of manual segmentation of lymph nodes, within an ultrasound image, is challenging due to operator dependency and speckle. A group of 23 healthy female volunteers consented to a short imaging session to capture a maximum of three axillary lymph nodes. A feasibility study was completed using both automatic and manual segmentation techniques to analyze a sample of 45, three-dimensional (3-D) nodal volume sets. Level-set segmentation based on geodesic active contours and shape-space learning based on a level-set segmentation approach was used to capture global node shapes. Most of the image feature based segmentation methods failed; however, a more precise automatic segmentation algorithm was obtained using a superimposed shape model. Shape model based segmentation significantly improved the segmentation compared with standard level sets. The best segmentation results were achieved when an experienced sonographer assisted with setting seed surfaces. The initialization of seed surfaces improved the capture of the global shape and lymphatic vessels.
The Ohio State University Department of Radiology, Columbus, OH, USA.
Steffen
S
Sammet
Kevin D
KD
Evans
M Okan
MO
Irfanoglu
Ashley
A
Strapp
Raghu
R
Machiraju
D000465:Algorithms; D001365:Axilla / Q000000981:diagnostic imaging*; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008198:Lymph Nodes / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography / Q000379:methods