PURPOSE: Automated 3D breast ultrasound (ABUS) has gained interest in breast imaging. Especially for screening women with dense breasts, ABUS appears to be beneficial. However, since the amount of data generated is large, the risk of oversight errors is substantial. Computer aided detection (CADe) may be used as a second reader to prevent oversight errors. When CADe is used in this fashion, it is essential that small cancers are detected, while the number of false positive findings should remain acceptable. In this work, the authors improve their previously developed CADe system in the initial candidate detection stage. METHODS: The authors use a large number of 2D Haar-like features to differentiate lesion structures from false positives. Using a cascade of GentleBoost classifiers that combines these features, a likelihood score, highly specific for small cancers, can be efficiently computed. The likelihood scores are added to the previously developed voxel features to improve detection. RESULTS: The method was tested in a dataset of 414 ABUS volumes with 211 cancers. Cancers had a mean size of 14.72 mm. Free-response receiver operating characteristic analysis was performed to evaluate the performance of the algorithm with and without using the aforementioned Haar-like feature likelihood scores. After the initial detection stage, the number of missed cancer was reduced by 18.8% after adding Haar-like feature likelihood scores. CONCLUSIONS: The proposed technique significantly improves our previously developed CADe system in the initial candidate detection stage.
PURPOSE: Automated 3D breast ultrasound (ABUS) has gained interest in breast imaging. Especially for screening women with dense breasts, ABUS appears to be beneficial. However, since the amount of data generated is large, the risk of oversight errors is substantial. Computer aided detection (CADe) may be used as a second reader to prevent oversight errors. When CADe is used in this fashion, it is essential that small cancers are detected, while the number of false positive findings should remain acceptable. In this work, the authors improve their previously developed CADe system in the initial candidate detection stage. METHODS: The authors use a large number of 2D Haar-like features to differentiate lesion structures from false positives. Using a cascade of GentleBoost classifiers that combines these features, a likelihood score, highly specific for small cancers, can be efficiently computed. The likelihood scores are added to the previously developed voxel features to improve detection. RESULTS: The method was tested in a dataset of 414 ABUS volumes with 211 cancers. Cancers had a mean size of 14.72 mm. Free-response receiver operating characteristic analysis was performed to evaluate the performance of the algorithm with and without using the aforementioned Haar-like feature likelihood scores. After the initial detection stage, the number of missed cancer was reduced by 18.8% after adding Haar-like feature likelihood scores. CONCLUSIONS: The proposed technique significantly improves our previously developed CADe system in the initial candidate detection stage.
Authors: Tao Tan; Zhang Li; Haixia Liu; Farhad G Zanjani; Quchang Ouyang; Yuling Tang; Zheyu Hu; Qiang Li Journal: IEEE J Transl Eng Health Med Date: 2018-08-16 Impact factor: 3.316
Authors: Afsaneh Jalalian; Syamsiah Mashohor; Rozi Mahmud; Babak Karasfi; M Iqbal B Saripan; Abdul Rahman B Ramli Journal: EXCLI J Date: 2017-02-20 Impact factor: 4.068
Authors: Jan C M van Zelst; Tao Tan; Paola Clauser; Angels Domingo; Monique D Dorrius; Daniel Drieling; Michael Golatta; Francisca Gras; Mathijn de Jong; Ruud Pijnappel; Matthieu J C M Rutten; Nico Karssemeijer; Ritse M Mann Journal: Eur Radiol Date: 2018-02-07 Impact factor: 5.315