Multi-Modality Computer-Aided Diagnosis System for AxillaryLymph Node (ALN) Staging: Segmentation of ALN on Ultrasound Images

摘要

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.