Estimating The Volume of Breast Tumor On The Digital Breast Tomosynthesis

TL;DR

This paper presents a new algorithm for estimating breast tumor volume from Digital Breast Tomosynthesis images, which could improve breast cancer risk assessment.

Contribution

It introduces an innovative segmentation and threshold optimization method for accurate tumor volume estimation in 3D breast images.

Findings

The algorithm effectively segments tumor regions in DBT images.

Optimal threshold determination improves volume estimation accuracy.

Potential to enhance breast cancer prognosis and risk classification.

Abstract

In this paper, we would like to quantitatively measure the tumor volume contained in the breast imaged by the Digital Breast Tomosynthesis (DBT), a reconstructed 3D image. The estimated volume will add to the prognostic value of risk classification of breast cancer. We develop an algorithm that offers an alternative way of estimating the volume of the tumor in a breast. We segment the region of interest by expressing the ratio of tumor region to normal region as a function of the threshold value in the image. Next, we determine the volume of the tumor region as a function of the threshold. We then find the optimal threshold value that yields the volume of the tumor contained in the breast with the rate of growth of the tumor volume function.