Automated quality assessment in three-dimensional breast ultrasound images.

Julia Schwaab,Johannes Gregori,Matthias Günther,Arnau Oliver,Jan Van Zelst,Albert Gubern-Mérida,Robert Martí,Yago Diez,Ahmed Bensouda Mourri

doi:10.1117/1.jmi.3.2.027002

Abstract

Automated three-dimensional breast ultrasound (ABUS) is a valuable adjunct to x-ray mammography for breast cancer screening of women with dense breasts. High image quality is essential for proper diagnostics and computer-aided detection. We propose an automated image quality assessment system for ABUS images that detects artifacts at the time of acquisition. Therefore, we study three aspects that can corrupt ABUS images: the nipple position relative to the rest of the breast, the shadow caused by the nipple, and the shape of the breast contour on the image. Image processing and machine learning algorithms are combined to detect these artifacts based on 368 clinical ABUS images that have been rated manually by two experienced clinicians. At a specificity of 0.99, 55% of the images that were rated as low quality are detected by the proposed algorithms. The areas under the ROC curves of the single classifiers are 0.99 for the nipple position, 0.84 for the nipple shadow, and 0.89 for the breast contour shape. The proposed algorithms work fast and reliably, which makes them adequate for online evaluation of image quality during acquisition. The presented concept may be extended to further image modalities and quality aspects.

Highlights

Three-dimensional (3-D) automated breast ultrasound (ABUS) is gaining importance in breast cancer screening programs as an adjunct to x-ray mammography.[1]
According to the acquisition protocol, the nipple position was indicated manually by the technicians after each measurement and stored in the DICOM header of the corresponding file so that it could be used for further image processing
We have shown that the proposed algorithms have the potential to detect up to 55% of images that are currently accepted but present diminished diagnostic value

Summary

Introduction

Three-dimensional (3-D) automated breast ultrasound (ABUS) is gaining importance in breast cancer screening programs as an adjunct to x-ray mammography.[1]. Recall rates of up to 19% due to BI-RADS category 0 rated images (Breast Imaging Reporting and Data System of the American College of Radiology) have been reported,[5] which means that these images were incomplete or of low quality and that a possible abnormality could not be clearly seen or defined These numbers can be explained by the fact that technicians need some time to train before they are able to produce artifact-free images since the. The positioning and compression of the breast are standardized to some extent and include anterior–posterior (AP), lateral (LAT), medial (MED), superior (SUP), or inferior (INF) views, the breast being gently pushed in these directions, respectively The latter one (INF) is acquired very rarely and was not contained in our datasets

Methods

Results

Discussion

Conclusion