Presentation + Paper
3 April 2023 An attentional unet with an auxiliary class learning to support acute ischemic stroke segmentation on CT
Author Affiliations +
Abstract
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.
Conference Presentation
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Santiago Gómez, Sebastian Florez, Daniel Mantilla, Paul Camacho, Nick Tarazona, and Fabio Martínez "An attentional unet with an auxiliary class learning to support acute ischemic stroke segmentation on CT", Proc. SPIE 12464, Medical Imaging 2023: Image Processing, 124640S (3 April 2023); https://doi.org/10.1117/12.2654269
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KEYWORDS
Ischemic stroke

Computed tomography

Education and training

Image segmentation

3D modeling

Tissues

Binary data

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