RATIONALE AND OBJECTIVES: The automated classification of sonographic breast lesions is generally accomplished by extracting and quantifying various features from the lesions. The selection of images to be analyzed, however, is usually left to the radiologist. Here we present an analysis of the effect that image selection can have on the performance of a breast ultrasound computer-aided diagnosis system. MATERIALS AND METHODS: A database of 344 different sonographic lesions was analyzed for this study (219 cysts/benign processes, 125 malignant lesions). The database was collected in an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant manner. Three different image selection protocols were used in the automated classification of each lesion: all images, first image only, and randomly selected images. After image selection, two different protocols were used to classify the lesions: (a) the average feature values were input to the classifier or (b) the classifier outputs were averaged together. Both protocols generated an estimated probability of malignancy. Round-robin analysis was performed using a Bayesian neural network-based classifier. Receiver-operating characteristic analysis was used to evaluate the performance of each protocol. Significance testing of the performance differences was performed via 95% confidence intervals and noninferiority tests. RESULTS: The differences in the area under the receiver-operating characteristic curves were never more than 0.02 for the primary protocols. Noninferiority was demonstrated between these protocols with respect to standard input techniques (all images selected and feature averaging). CONCLUSION: We have proved that our automated lesion classification scheme is robust and can perform well when subjected to variations in user input.
RATIONALE AND OBJECTIVES: The automated classification of sonographic breast lesions is generally accomplished by extracting and quantifying various features from the lesions. The selection of images to be analyzed, however, is usually left to the radiologist. Here we present an analysis of the effect that image selection can have on the performance of a breast ultrasound computer-aided diagnosis system. MATERIALS AND METHODS: A database of 344 different sonographic lesions was analyzed for this study (219 cysts/benign processes, 125 malignant lesions). The database was collected in an institutional review board-approved, Health Insurance Portability and Accountability Act-compliant manner. Three different image selection protocols were used in the automated classification of each lesion: all images, first image only, and randomly selected images. After image selection, two different protocols were used to classify the lesions: (a) the average feature values were input to the classifier or (b) the classifier outputs were averaged together. Both protocols generated an estimated probability of malignancy. Round-robin analysis was performed using a Bayesian neural network-based classifier. Receiver-operating characteristic analysis was used to evaluate the performance of each protocol. Significance testing of the performance differences was performed via 95% confidence intervals and noninferiority tests. RESULTS: The differences in the area under the receiver-operating characteristic curves were never more than 0.02 for the primary protocols. Noninferiority was demonstrated between these protocols with respect to standard input techniques (all images selected and feature averaging). CONCLUSION: We have proved that our automated lesion classification scheme is robust and can perform well when subjected to variations in user input.
Authors: Nicholas P Gruszauskas; Karen Drukker; Maryellen L Giger; Ruey-Feng Chang; Charlene A Sennett; Woo Kyung Moon; Lorenzo L Pesce Journal: Radiology Date: 2009-10-28 Impact factor: 11.105
Authors: Christopher W Haddad; Karen Drukker; Rebecca Gullett; Timothy J Carroll; Gregory A Christoforidis; Maryellen L Giger Journal: J Med Imaging (Bellingham) Date: 2018-01-04
Authors: Karen Drukker; Fred Duewer; Maryellen L Giger; Serghei Malkov; Chris I Flowers; Bonnie Joe; Karla Kerlikowske; Jennifer S Drukteinis; Hui Li; John A Shepherd Journal: Med Phys Date: 2014-03 Impact factor: 4.071