Limitations of Automated Segmentation Tools for Breast Tissue in Young Women Treated with Radiotherapy to the Chest

H. Chamberlin, E. Henderson, R. Cowan, C. Anandadas, C. Hague, F. Wilson, J. Radford, S. Howell, S. Astley, M. Aznar, E. Vasquez Osorio

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The performance of commercially available segmentation tools (deep learning and atlas-based) were assessed for breast contouring of young lymphoma patients on CT. Dice similarity coefficient, mean distance to agreement and Hausdorff distance were used to analyse performance. Deep learning segmentation performed better on more patients (6/10) but atlas-based segmentation performed best on 2/10. The variation of breast densities and arm positions likely affects auto-contouring performance in young lymphoma patients, as atlas libraries struggle to encompass the wide variation, and deep learning training data is typically of older breast cancer patients.

Original languageEnglish
Title of host publication2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
PublisherIEEE Computer Society
ISBN (Electronic)9781665473583
DOIs
Publication statusPublished - 2023
Event20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia
Duration: 18 Apr 202321 Apr 2023

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2023-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/TerritoryColombia
CityCartagena
Period18/04/2321/04/23

Keywords

  • atlas-based segmentation
  • deep-learning segmentation
  • lymphoma
  • radiotherapy
  • segmentation

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