Self- and Cross-attention based Transformer for left ventricle segmentation in 4D flow MRIDownload PDF

Published: 09 May 2022, Last Modified: 12 May 2023MIDL 2022 Short PapersReaders: Everyone
Keywords: 4D flow MRI, segmentation, transformer, self-attention, cross-attention
TL;DR: Transformer based left ventricle segmentation in 4D flow MRI
Abstract: The conventional quantitative analysis of 4D flow MRI relies on the co-registered cine MRI. In this work, we proposed a self- and cross-attention based Transformer to segment the left ventricle directly from the 4D flow MRI and evaluated our method on a large dataset using various metrics. The results demonstrate that self- and cross-attention improve the segmentation performance, achieving a mean Dice of 82.41$\%$, ASD of 4.51 mm, left ventricle ejection fraction (LVEF) error of 7.96$\%$ and kinetic energy (KE) error of 1.34 $\mu$J$/$ml.
Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Segmentation
Secondary Subject Area: Application: Other
Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
1 Reply

Loading