LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data

Purpose This study aims at developing and evaluating a prototype of a lesion candidate detection algorithm for a 3D-US computer-aided diagnosis (CAD) system. Methods Additionally, to routine imaging, automated breast volume scans (ABVS) were performed on 63 patients. All ABVS exams were analyzed and...

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Bibliographic Details
Published in:Archives of gynecology and obstetrics 2016-08, Vol.294 (2), p.423-428
Main Authors: Golatta, Michael, Zeegers, Désirée, Filippatos, Konstantinos, Binder, Leah-Larissa, Scharf, Alexander, Rauch, Geraldine, Rom, Joachim, Schütz, Florian, Sohn, Christof, Heil, Joerg
Format: Article
Language:English
Subjects:
Algorithms
Breast cancer
Breast Neoplasms - diagnostic imaging
Diagnosis, Computer-Assisted - methods
Endocrinology
Female
Gynecology
Human Genetics
Humans
Image Enhancement
Images in Obstetrics and Gynecology
Imaging, Three-Dimensional - methods
Medicine
Medicine & Public Health
Obstetrics/Perinatology/Midwifery
Sensitivity and Specificity
Software
Ultrasonic imaging
Ultrasonography - methods
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Summary:Purpose This study aims at developing and evaluating a prototype of a lesion candidate detection algorithm for a 3D-US computer-aided diagnosis (CAD) system. Methods Additionally, to routine imaging, automated breast volume scans (ABVS) were performed on 63 patients. All ABVS exams were analyzed and annotated before the evaluation with different algorithm blob detectors characterized by different blob-radiuses, voxel-sizes and the quantiles of blob filter responses to find lesion candidates. Lesions found in candidates were compared to the prior annotations. Results All histologically proven lesions were detected with at least one algorithm. The algorithm with optimal sensitivity detected all cancers (sensitivity = 100 %) with a very low positive predictive value due to a high false-positive rate. Conclusions ABVS is a new technology which can be analyzed by a CAD software. Using different algorithms, lesions can be detected with a very high and accurate sensitivity. Further research for feature extraction and lesion classification is needed aiming at reducing the false-positive hits.
ISSN:0932-0067
1432-0711
DOI:10.1007/s00404-016-4127-5