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Annotation and characterization of lesions in breast tomosynthesis images

Dustler, Magnus LU orcid ; Ohashi, Akane LU orcid ; Tomic, Hanna LU ; Johnson, Kristin LU orcid ; Zackrisson, Sophia LU ; Tingberg, Anders LU orcid and Bakic, Predrag R. LU (2026) In Radiation Protection Dosimetry 202(3-4). p.326-330
Abstract

Rapid adoption of artificial intelligence methods in breast imaging research emphasizes the need for large, appropriately curated image databases for development and validation. For digital breast tomosynthesis (DBT), there are few public databases with only limited lesion annotation. Recently, we have developed Malmö Breast ImaginG (M-BIG), a large database of 104 791 women screened at Skåne University Hospital, Malmö. M-BIG also includes all images from the Malmö Breast Tomosynthesis Screening Trial, MBTST of 14 848 women, with 139 biopsy-confirmed cancers from DBT screening. To annotate lesions in M-BIG, we designed a semi-automated custom software tool for DBT, and corresponding digital mammography (DM) images. A reader manually... (More)

Rapid adoption of artificial intelligence methods in breast imaging research emphasizes the need for large, appropriately curated image databases for development and validation. For digital breast tomosynthesis (DBT), there are few public databases with only limited lesion annotation. Recently, we have developed Malmö Breast ImaginG (M-BIG), a large database of 104 791 women screened at Skåne University Hospital, Malmö. M-BIG also includes all images from the Malmö Breast Tomosynthesis Screening Trial, MBTST of 14 848 women, with 139 biopsy-confirmed cancers from DBT screening. To annotate lesions in M-BIG, we designed a semi-automated custom software tool for DBT, and corresponding digital mammography (DM) images. A reader manually draws an outline; or marks nodes around the lesion which are automatically connected by an edge-following algorithm. Our custom tool enables detailed annotation of DBT and DM lesions, as opposed to the rectangular regions present in other published material, allowing extensive evaluation of tumor segmentation, and analysis of size and shape descriptors.

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Please use this url to cite or link to this publication:
author
; ; ; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Radiation Protection Dosimetry
volume
202
issue
3-4
pages
5 pages
publisher
Oxford University Press
external identifiers
  • pmid:41821447
  • scopus:105032798600
ISSN
0144-8420
DOI
10.1093/rpd/ncaf177
language
English
LU publication?
yes
additional info
Publisher Copyright: © The Author(s) 2026. Published by Oxford University Press.
id
662ba609-48ce-463f-8b45-7a5587d3aa20
date added to LUP
2026-04-23 14:47:58
date last changed
2026-04-24 03:06:33
@article{662ba609-48ce-463f-8b45-7a5587d3aa20,
  abstract     = {{<p>Rapid adoption of artificial intelligence methods in breast imaging research emphasizes the need for large, appropriately curated image databases for development and validation. For digital breast tomosynthesis (DBT), there are few public databases with only limited lesion annotation. Recently, we have developed Malmö Breast ImaginG (M-BIG), a large database of 104 791 women screened at Skåne University Hospital, Malmö. M-BIG also includes all images from the Malmö Breast Tomosynthesis Screening Trial, MBTST of 14 848 women, with 139 biopsy-confirmed cancers from DBT screening. To annotate lesions in M-BIG, we designed a semi-automated custom software tool for DBT, and corresponding digital mammography (DM) images. A reader manually draws an outline; or marks nodes around the lesion which are automatically connected by an edge-following algorithm. Our custom tool enables detailed annotation of DBT and DM lesions, as opposed to the rectangular regions present in other published material, allowing extensive evaluation of tumor segmentation, and analysis of size and shape descriptors.</p>}},
  author       = {{Dustler, Magnus and Ohashi, Akane and Tomic, Hanna and Johnson, Kristin and Zackrisson, Sophia and Tingberg, Anders and Bakic, Predrag R.}},
  issn         = {{0144-8420}},
  language     = {{eng}},
  month        = {{03}},
  number       = {{3-4}},
  pages        = {{326--330}},
  publisher    = {{Oxford University Press}},
  series       = {{Radiation Protection Dosimetry}},
  title        = {{Annotation and characterization of lesions in breast tomosynthesis images}},
  url          = {{http://dx.doi.org/10.1093/rpd/ncaf177}},
  doi          = {{10.1093/rpd/ncaf177}},
  volume       = {{202}},
  year         = {{2026}},
}