Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering

Ana Mota, Matthew Clarkson, Pedro Almeida, Nuno Matela

2022

Abstract

Microcalcification clusters (MCs) are one of the most important biomarkers for breast cancer and Digital Breast Tomosynthesis (DBT) has consolidated its role in breast cancer imaging. As there are mixed observations about MCs detection using DBT, it is important to develop tools that improve this task. Furthermore, the visualization mode of MCs is also crucial, as their diagnosis is associated with their 3D morphology. In this work, DBT data from a public database were used to train a faster region-based convolutional neural network (R-CNN) to locate MCs in entire DBT. Additionally, the detected MCs were further analyzed through standard 2D visualization and 3D volume rendering (VR) specifically developed for DBT data. For MCs detection, the sensitivity of our Faster R-CNN was 60% with 4 false positives. These preliminary results are very promising and can be further improved. On the other hand, the 3D VR visualization provided important information, with higher quality and discernment of the detected MCs. The developed pipeline may help radiologists since (1) it indicates specific breast regions with possible lesions that deserve additional attention and (2) as the rendering of the MCs is similar to a segmentation, a detailed complementary analysis of their 3D morphology is possible.

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Paper Citation


in Harvard Style

Mota A., Clarkson M., Almeida P. and Matela N. (2022). Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING, ISBN 978-989-758-552-4, pages 80-89. DOI: 10.5220/0010938800003123


in Bibtex Style

@conference{bioimaging22,
author={Ana Mota and Matthew Clarkson and Pedro Almeida and Nuno Matela},
title={Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,},
year={2022},
pages={80-89},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010938800003123},
isbn={978-989-758-552-4},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOIMAGING,
TI - Detection of Microcalcifications in Digital Breast Tomosynthesis using Faster R-CNN and 3D Volume Rendering
SN - 978-989-758-552-4
AU - Mota A.
AU - Clarkson M.
AU - Almeida P.
AU - Matela N.
PY - 2022
SP - 80
EP - 89
DO - 10.5220/0010938800003123