A weakly labeled approach for breast tissue segmentation and breast density estimation in digital mammography

Abstract

Breast tissue segmentation is a fundamental task in digital mammography. Commonly, this segmentation is applied prior to breast density estimation. However, observations show a strong correlation between the segmentation parameters and the breast density, resulting in a chicken and egg problem. This paper presents a new method for breast segmentation, based on training with weakly labeled data, namely breast density categories. To this end, a Fuzzy-logic module is employed computing an adaptive parameter for segmentation. The suggested scheme consists of a feedback stage where a preliminary segmentation is used to allow extracting domain specific features from an early estimation of the tissue regions. Selected features are then fed into a fuzzy logic module to yield an updated threshold for segmentation. Our evaluation is based on 50 fibroglandular delineated images and on breast density classification, obtained on a large data set of 1243 full-field digital mammograms. The data set contained images from different devices. The proposed analysis provided an average Jaccard spatial similarity coefficient of 0.4 with improvement of this measure in 70% of cases where the suggested module was applied. In breast density classification, average classification accuracy of 75% was obtained, which significantly improved the baseline method (67.4%). Major improvement is obtained in low breast densities where higher threshold levels rejects false positive regions. These results show a promise for the clinical application of this method in breast segmentation, without the need for laborious tissue annotation.