A 2‐Phase Merge Filter Approach to Computer‐Aided Detection of Breast Tumors on 3‐Dimensional Ultrasound Imaging

• Published in: Journal of ultrasound in medicine Vol. 39; no. 12; pp. 2439 - 2455
• Main Authors: Chiu, Ling‐Ying, Kuo, Wen‐Hung, Chen, Chiung‐Nien, Chang, King‐Jen, Chen, Argon
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.12.2020
• Subjects: 2‐phase merge filter; 3‐dimensional automated breast ultrasound; Algorithms; Breast - diagnostic imaging; Breast Neoplasms - diagnostic imaging; breast tumor; Computers; computer‐aided detection; Female; Humans; Sensitivity and Specificity; Ultrasonography, Mammary; breast tumor; computer-aided detection; 3-dimensional automated breast ultrasound; 2-phase merge filter
• DOI: 10.1002/jum.15365
• ISSN: 0278-4297; 1550-9613

Objectives The role of image analysis in 3‐dimensional (3D) automated breast ultrasound (ABUS) images is increasingly important because of its widespread use as a screening tool in whole‐breast examinations. However, reviewing a large number of images acquired from ABUS is time‐consuming and sometimes error prone. The aim of this study, therefore, was to develop an efficient computer‐aided detection (CADe) algorithm to assist the review process. Methods The proposed CADe algorithm consisted of 4 major steps. First, initial tumor candidates were formed by extracting and merging hypoechoic square cells on 2‐dimensional (2D) transverse images. Second, a feature‐based classifier was then constructed using 2D features to filter out nontumor candidates. Third, the remaining 2D candidates were merged longitudinally into 3D masses. Finally, a 3D feature‐based classifier was used to further filter out nontumor masses to obtain the final detected masses. The proposed method was validated with 176 passes of breast images acquired by an Acuson S2000 automated breast volume scanner (Siemens Medical Solutions USA, Inc., Malvern, PA), including 44 normal passes and 132 abnormal passes containing 162 proven lesions (79 benign and 83 malignant). Results The proposed CADe system could achieve overall sensitivity of 100% and 90% with 6.71 and 5.14 false‐positives (FPs) per pass, respectively. Our results also showed that the average number of FPs per normal pass (7.16) was more than the number of FPs per abnormal pass (6.56) at 100% sensitivity. Conclusions The proposed CADe system has a great potential for becoming a good companion tool with ABUS imaging by ensuring high sensitivity with a relatively small number of FPs.