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3 March 2009 Multi-modality computer-aided diagnosis system for axillary lymph node (ALN) staging: segmentation of ALN on ultrasound images
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Proceedings Volume 7260, Medical Imaging 2009: Computer-Aided Diagnosis; 72602D (2009) https://doi.org/10.1117/12.811212
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States
Abstract
Our goal was to develop and evaluate a reliable segmentation method to delineate axillary lymph node (ALN) from surrounding tissues on US images as the first step of building a multi-modality CADx system for staging ALN. Ultrasound images of 24 ALN from 18 breast cancer patients were used. An elliptical model algorithm was used to fit ALNs boundaries using the following steps: reduce image noise, extract image edges using the Canny edge detector, select edge pixels and fit an ellipse by minimizing the quadratic error, Find the best fitting ellipse based on RANSAC. The segmentation was qualitatively evaluated by 3 expert readers using 4 aspects: Orientation of long axis (OLA): within +- 45 degrees, or off by +-45 degrees, overlap (OV): the fitted ellipse completely included ALN, partially included ALN, or missed the ALN, size (SZ): too small, good within 20% error margin, or too large, and aspect ratio (AR): correct or wrong. Nightly six % of ALNs were correctly evaluated by all readers in terms of OLA and AR, 90.2% in terms of OV and 86.11 in terms of SZ. Readers agreed that the segmentation was correct in 70% of the cases in all aspects. Due to small sample size and small variation among readers, we don't have power to show the accuracy of them is different.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lina Arbash Meinel, Martin Bergtholdt, Hiroyuki Abe, D. Huo, Thomas Buelow, Ingwer Carlsen, and Gillian Newstead "Multi-modality computer-aided diagnosis system for axillary lymph node (ALN) staging: segmentation of ALN on ultrasound images", Proc. SPIE 7260, Medical Imaging 2009: Computer-Aided Diagnosis, 72602D (3 March 2009); https://doi.org/10.1117/12.811212
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