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

Standard

LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data. / Golatta, Michael; Zeegers, Désirée; Filippatos, Konstantinos; Binder, Leah-Larissa; Scharf, Alexander; Rauch, Geraldine; Rom, Joachim; Schütz, Florian; Sohn, Christof; Heil, Joerg.

In: ARCH GYNECOL OBSTET, Vol. 294, No. 2, 08.2016, p. 423-428.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Golatta, M, Zeegers, D, Filippatos, K, Binder, L-L, Scharf, A, Rauch, G, Rom, J, Schütz, F, Sohn, C & Heil, J 2016, 'LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data', ARCH GYNECOL OBSTET, vol. 294, no. 2, pp. 423-428. https://doi.org/10.1007/s00404-016-4127-5

APA

Golatta, M., Zeegers, D., Filippatos, K., Binder, L-L., Scharf, A., Rauch, G., Rom, J., Schütz, F., Sohn, C., & Heil, J. (2016). LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data. ARCH GYNECOL OBSTET, 294(2), 423-428. https://doi.org/10.1007/s00404-016-4127-5

Vancouver

Golatta M, Zeegers D, Filippatos K, Binder L-L, Scharf A, Rauch G et al. LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data. ARCH GYNECOL OBSTET. 2016 Aug;294(2):423-428. https://doi.org/10.1007/s00404-016-4127-5

Bibtex

@article{98a8e5df5524466e961b1d0b20b9bdeb,
title = "LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data",
abstract = "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.",
author = "Michael Golatta and D{\'e}sir{\'e}e Zeegers and Konstantinos Filippatos and Leah-Larissa Binder and Alexander Scharf and Geraldine Rauch and Joachim Rom and Florian Sch{\"u}tz and Christof Sohn and Joerg Heil",
year = "2016",
month = aug,
doi = "10.1007/s00404-016-4127-5",
language = "English",
volume = "294",
pages = "423--428",
journal = "ARCH GYNECOL OBSTET",
issn = "0932-0067",
publisher = "Springer",
number = "2",

}

RIS

TY - JOUR

T1 - LECANDUS study (LEsion CANdidate Detection in UltraSound Data): evaluation of image analysis algorithms for breast lesion detection in volume ultrasound data

AU - Golatta, Michael

AU - Zeegers, Désirée

AU - Filippatos, Konstantinos

AU - Binder, Leah-Larissa

AU - Scharf, Alexander

AU - Rauch, Geraldine

AU - Rom, Joachim

AU - Schütz, Florian

AU - Sohn, Christof

AU - Heil, Joerg

PY - 2016/8

Y1 - 2016/8

N2 - 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.

AB - 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.

U2 - 10.1007/s00404-016-4127-5

DO - 10.1007/s00404-016-4127-5

M3 - SCORING: Journal article

C2 - 27236704

VL - 294

SP - 423

EP - 428

JO - ARCH GYNECOL OBSTET

JF - ARCH GYNECOL OBSTET

SN - 0932-0067

IS - 2

ER -