Computerized lesion detection on breast ultrasound

TLDR

Computerized analysis of breast sonograms could enhance sonography’s role in breast cancer screening. The study investigates using a radial gradient index filtering technique to automatically detect lesions on breast ultrasound. The method applies radial gradient index filtering, segments candidates by maximizing average radial gradient, and classifies them with a Bayesian neural network evaluated through round‑robin analysis. The approach achieved 87 % sensitivity at 0.76 false positives per image after RGI filtering, 75 % correct detection at 0.4 overlap, and overall 94 % sensitivity at 0.48 false positives per image with the Bayesian neural network.

Abstract

We investigated the use of a radial gradient index (RGI) filtering technique to automatically detect lesions on breast ultrasound. After initial RGI filtering, a sensitivity of 87% at 0.76 false‐positive detections per image was obtained on a database of 400 patients (757 images). Next, lesion candidates were segmented from the background by maximizing an average radial gradient (ARD) index for regions grown from the detected points. At an overlap of 0.4 with a radiologist lesion outline, 75% of the lesions were correctly detected. Subsequently, round robin analysis was used to assess the quality of the classification of lesion candidates into actual lesions and false‐positives by a Bayesian neural network. The round robin analysis yielded an value of 0.84, and an overall performance by case of 94% sensitivity at 0.48 false‐positives per image. Use of computerized analysis of breast sonograms may ultimately facilitate the use of sonography in breast cancer screening programs.