Algorithm to estimate the level of elasticity of biological tissue with ultrasound elastography images

Díaz, Jhoana; Castellanos, N.P.; Pineda, Carlos; Hernandez, Cristina; Ventura, L.; Gutierrez, J.

doi:10.1109/pahce.2015.7173317

Cited by 1 publication

(1 citation statement)

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“…However, images can also be displayed in color scale, in which the color spectrum typically goes from blue tissue (high strain) to red (no strain), that is, from the soft to hard lesions, respectively, with an intermediate level green (with a medium level of strain). The color scale may vary depending on the ultrasound manufacturer [ 14 ]. Many studies have reported that it can increase the specificity of conventional B-mode ultrasound in differentiating benign from malignant masses, reducing the number of benign breast biopsy results [ 2 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Computer-Aided Diagnosis System in the Classification of Lesions in Breast Strain Elastography Imaging

et al. 2018

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Purpose: Evaluation of the performance of a computer-aided diagnosis (CAD) system based on the quantified color distribution in strain elastography imaging to evaluate the malignancy of breast tumors. Methods: The database consisted of 31 malignant and 52 benign lesions. A radiologist who was blinded to the diagnosis performed the visual analysis of the lesions. After six months with no eye contact on the breast images, the same radiologist and other two radiologists manually drew the contour of the lesions in B-mode ultrasound, which was masked in the elastography image. In order to measure the amount of hard tissue in a lesion, we developed a CAD system able to identify the amount of hard tissue, represented by red color, and quantify its predominance in a lesion, allowing classification as soft, intermediate, or hard. The data obtained with the CAD system were compared with the visual analysis. We calculated the sensitivity, specificity, and area under the curve (AUC) for the classification using the CAD system from the manual delineation of the contour by each radiologist. Results: The performance of the CAD system for the most experienced radiologist achieved sensitivity of 70.97%, specificity of 88.46%, and AUC of 0.853. The system presented better performance compared with his visual diagnosis, whose sensitivity, specificity, and AUC were 61.29%, 88.46%, and 0.829, respectively. The system obtained sensitivity, specificity, and AUC of 67.70%, 84.60%, and 0.783, respectively, for images segmented by Radiologist 2, and 51.60%, 92.30%, and 0.771, respectively, for those segmented by the Resident. The intra-class correlation coefficient was 0.748. The inter-observer agreement of the CAD system with the different contours was good in all comparisons. Conclusions: The proposed CAD system can improve the radiologist performance for classifying breast masses, with excellent inter-observer agreement. It could be a promising tool for clinical use.

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Section: Introductionmentioning

confidence: 99%