An attentional Unet with an auxiliary class learning to support acute ischemic stroke segmentation on CT

Santiago Gómez, Sebastian Florez, Daniel Mantilla, Paul Camacho, Nick Tarazona, Fabio Martínez

Research output: Book / Book Chapter / ReportResearch Bookspeer-review


Computed tomography (CT) is the first-line imaging modality for evaluation of patients suspected of stroke. Specially, such modality is key as screening test between ischemia and hemorrhage strokes. Despite remarkable support of encoder-decoder architectures, the delineation of ischemic lesions remains challenging on CT studies, reporting poor sensitivity, especially in the acute stage. Among others, these nets are affected because of the low scan quality, the challenging stroke geometry, and the variable textural representation. This work introduces a boundary-focused attention U-Net that takes advantage of cross-attention mechanism, that along multiple levels allows to recover stroke segmentation on CT scans. The proposed architecture is enriched with skip connections, that help in the recovering of saliency lesion maps and motivated the preservation of morphology. Besides, an auxiliary class is herein introduced with a weighted special loss function that remark lesion tissue, alleviating the negative impact of class unbalance. The proposed approach was validated on the public ISLES2018 dataset achieving an average dice score of 0.42 and a precision of 0.48.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationImage Processing
EditorsOlivier Colliot, Ivana Isgum
ISBN (Electronic)9781510660335
StatePublished - 2023
Externally publishedYes
EventMedical Imaging 2023: Image Processing - San Diego, United States
Duration: 19 Feb 202323 Feb 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2023: Image Processing
Country/TerritoryUnited States
CitySan Diego


  • Attention Deep Learning
  • Computed Tomography
  • Lesion Boundaries
  • Medical Image Segmentation
  • Stroke


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