Breast density assessment using breast tomosynthesis images
(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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- author
- Timberg, Pontus LU ; Fieselmann, Andreas ; Dustler, Magnus LU ; Petersson, Hannie LU ; Sartor, Hanna LU ; Lång, Kristina LU ; Förnvik, Daniel LU and Zackrisson, Sophia LU
- organization
- publishing date
- 2016
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- keywords
- BI-RADS, Breast density, Breast tomosynthesis, Mammography
- host publication
- Breast Imaging : 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings - 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings
- series title
- Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- editor
- Tingberg, Anders ; Lång, Kristina and Timberg, Pontus
- volume
- 9699
- pages
- 6 pages
- publisher
- Springer
- conference name
- 13th International Workshop on Breast Imaging, IWDM 2016
- conference location
- Malmo, Sweden
- conference dates
- 2016-06-19 - 2016-06-22
- external identifiers
-
- scopus:84977600472
- wos:000386324200026
- ISSN
- 03029743
- 16113349
- ISBN
- 9783319415451
- 978-3-319-41546-8
- 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
- 2024-06-28 12:51:31
@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 = {{Breast Imaging : 13th International Workshop, IWDM 2016, Malmö, Sweden, June 19-22, 2016, Proceedings}}, editor = {{Tingberg, Anders and Lång, Kristina and Timberg, Pontus}}, isbn = {{9783319415451}}, issn = {{03029743}}, keywords = {{BI-RADS; Breast density; Breast tomosynthesis; Mammography}}, language = {{eng}}, pages = {{197--202}}, publisher = {{Springer}}, series = {{Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)}}, title = {{Breast density assessment using breast tomosynthesis images}}, url = {{http://dx.doi.org/10.1007/978-3-319-41546-8_26}}, doi = {{10.1007/978-3-319-41546-8_26}}, volume = {{9699}}, year = {{2016}}, }