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19 November 2018 Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and computer-aided detection comparison
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Abstract
Mammographic computer-aided detection (CADe) devices are typically first developed and assessed for a specific “original” acquisition system. When developers are ready to apply their CADe device to a mammographic acquisition system, they typically assess the device with images acquired using the system. Collecting large repositories of clinical images containing verified lesion locations acquired by a system is costly and time consuming. We previously developed an image blending technique that allows users to seamlessly insert regions of interest (ROIs) from one medical image into another image. Our goal is to assess the performance of this technique for inserting microcalcification clusters from one mammogram into another, with the idea that when fully developed, our technique may be useful for reducing the clinical data burden in the assessment of a CADe device for use with an image acquisition system. We first perform a reader study to assess whether experienced observers can distinguish between computationally inserted and native clusters. For this purpose, we apply our insertion technique to 55 clinical cases. ROIs containing microcalcification clusters from one breast of a patient are inserted into the contralateral breast of the same patient. The analysis of the reader ratings using receiver operating characteristic (ROC) methodology indicates that inserted clusters cannot be reliably distinguished from native clusters (area under the ROC curve  =  0.58  ±  0.04). Furthermore, CADe sensitivity is evaluated on mammograms of 68 clinical cases with native and inserted microcalcification clusters using a commercial CADe system. The average by-case sensitivities for native and inserted clusters are equal, 85.3% (58/68). The average by-image sensitivities for native and inserted clusters are 72.3% and 67.6%, respectively, with a difference of 4.7% and a 95% confidence interval of [−2.1 11.6]. These results demonstrate the potential for using the inserted microcalcification clusters for assessing mammographic CADe devices.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2018/$25.00 © 2018 SPIE
Zahra Ghanian, Aria Pezeshk, Nicholas Petrick, and Berkman Sahiner "Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and computer-aided detection comparison," Journal of Medical Imaging 5(4), 044502 (19 November 2018). https://doi.org/10.1117/1.JMI.5.4.044502
Received: 25 May 2018; Accepted: 10 October 2018; Published: 19 November 2018
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