Literature DB >> 30840741

Computational insertion of microcalcification clusters on mammograms: reader differentiation from native clusters and computer-aided detection comparison.

Zahra Ghanian1, Aria Pezeshk1, Nicholas Petrick1, Berkman Sahiner1.   

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.

Entities:  

Keywords:  computer-aided detection; digital mammography; microcalcification insertion

Year:  2018        PMID: 30840741      PMCID: PMC6241543          DOI: 10.1117/1.JMI.5.4.044502

Source DB:  PubMed          Journal:  J Med Imaging (Bellingham)        ISSN: 2329-4302


  7 in total

1.  Simulation of mammographic lesions.

Authors:  Robert Saunders; Ehsan Samei; Jay Baker; David Delong
Journal:  Acad Radiol       Date:  2006-07       Impact factor: 3.173

2.  Note on the sampling error of the difference between correlated proportions or percentages.

Authors:  Q McNEMAR
Journal:  Psychometrika       Date:  1947-06       Impact factor: 2.500

3.  A generic framework to simulate realistic lung, liver and renal pathologies in CT imaging.

Authors:  Justin Solomon; Ehsan Samei
Journal:  Phys Med Biol       Date:  2014-10-17       Impact factor: 3.609

4.  Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach.

Authors:  E R DeLong; D M DeLong; D L Clarke-Pearson
Journal:  Biometrics       Date:  1988-09       Impact factor: 2.571

5.  Seamless Lesion Insertion for Data Augmentation in CAD Training.

Authors:  Aria Pezeshk; Nicholas Petrick; Berkman Sahiner
Journal:  IEEE Trans Med Imaging       Date:  2016-12-14       Impact factor: 10.048

6.  Seamless Insertion of Pulmonary Nodules in Chest CT Images.

Authors:  Aria Pezeshk; Berkman Sahiner; Rongping Zeng; Adam Wunderlich; Weijie Chen; Nicholas Petrick
Journal:  IEEE Trans Biomed Eng       Date:  2015-06-12       Impact factor: 4.538

7.  An evaluation of image descriptors combined with clinical data for breast cancer diagnosis.

Authors:  Daniel C Moura; Miguel A Guevara López
Journal:  Int J Comput Assist Radiol Surg       Date:  2013-04-13       Impact factor: 2.924

  7 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.