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Breast density assessment using breast tomosynthesis images

Timberg, Pontus LU ; Fieselmann, Andreas; Dustler, Magnus LU ; Petersson, Hannie LU ; Sartor, Hanna LU ; Lång, Kristina LU ; Förnvik, Daniel LU and Zackrisson, Sophia LU (2016) 13th International Workshop on Breast Imaging, IWDM 2016 In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 9699. p.197-202
Abstract

In this work we evaluate an approach for breast density assessment of digital breast tomosynthesis (DBT) data using the central projection image. A total of 348 random cases (both FFDM CC and MLO views and DBT MLO views) were collected using a Siemens Mammomat Inspiration tomosynthesis unit at Unilabs, Malmö. The cases underwent both BI-RADS 5th Edition labeling by radiologists and automated volumetric breast density analysis (VBDA) by an algorithm. Preliminary results showed an observed agreement of 70% (weighted Kappa, κ = 0.73) between radiologists and VBDA using FFDM images and 63% (κ = 0.62) for radiologists and VBDA using DBT images. Comparison between densities for FFDM and DBT resulted in high correlation (r = 0.94) and an... (More)

In this work we evaluate an approach for breast density assessment of digital breast tomosynthesis (DBT) data using the central projection image. A total of 348 random cases (both FFDM CC and MLO views and DBT MLO views) were collected using a Siemens Mammomat Inspiration tomosynthesis unit at Unilabs, Malmö. The cases underwent both BI-RADS 5th Edition labeling by radiologists and automated volumetric breast density analysis (VBDA) by an algorithm. Preliminary results showed an observed agreement of 70% (weighted Kappa, κ = 0.73) between radiologists and VBDA using FFDM images and 63% (κ = 0.62) for radiologists and VBDA using DBT images. Comparison between densities for FFDM and DBT resulted in high correlation (r = 0.94) and an observed agreement of 72% (κ = 0.76). The automated analysis is a promising approach using low dose central projection DBT images in order to get radiologist- like density ratings similar to results obtained from FFDM.

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Please use this url to cite or link to this publication:
author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
BI-RADS, Breast density, Breast tomosynthesis, Mammography
in
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
editor
Tingberg, Anders ; Lång, Kristina; Timberg, Pontus; ; and
volume
9699
pages
6 pages
publisher
Springer Verlag
conference name
13th International Workshop on Breast Imaging, IWDM 2016
external identifiers
  • scopus:84977600472
ISSN
16113349
03029743
ISBN
9783319415451
DOI
10.1007/978-3-319-41546-8_26
language
English
LU publication?
yes
id
c9035703-8b32-4baa-b4fe-b1d460a1b4aa
date added to LUP
2016-07-25 12:44:01
date last changed
2017-02-15 13:22:54
@inproceedings{c9035703-8b32-4baa-b4fe-b1d460a1b4aa,
  abstract     = {<p>In this work we evaluate an approach for breast density assessment of digital breast tomosynthesis (DBT) data using the central projection image. A total of 348 random cases (both FFDM CC and MLO views and DBT MLO views) were collected using a Siemens Mammomat Inspiration tomosynthesis unit at Unilabs, Malmö. The cases underwent both BI-RADS 5th Edition labeling by radiologists and automated volumetric breast density analysis (VBDA) by an algorithm. Preliminary results showed an observed agreement of 70% (weighted Kappa, κ = 0.73) between radiologists and VBDA using FFDM images and 63% (κ = 0.62) for radiologists and VBDA using DBT images. Comparison between densities for FFDM and DBT resulted in high correlation (r = 0.94) and an observed agreement of 72% (κ = 0.76). The automated analysis is a promising approach using low dose central projection DBT images in order to get radiologist- like density ratings similar to results obtained from FFDM.</p>},
  author       = {Timberg, Pontus and Fieselmann, Andreas and Dustler, Magnus and Petersson, Hannie and Sartor, Hanna and Lång, Kristina and Förnvik, Daniel and Zackrisson, Sophia},
  booktitle    = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  editor       = {Tingberg, Anders  and Lång, Kristina and Timberg, Pontus},
  isbn         = {9783319415451},
  issn         = {16113349},
  keyword      = {BI-RADS,Breast density,Breast tomosynthesis,Mammography},
  language     = {eng},
  pages        = {197--202},
  publisher    = {Springer Verlag},
  title        = {Breast density assessment using breast tomosynthesis images},
  url          = {http://dx.doi.org/10.1007/978-3-319-41546-8_26},
  volume       = {9699},
  year         = {2016},
}