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

22 Apr 2022, 16:34 (modified: 04 Jun 2022, 12:07)MIDL 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.
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Paper Type: novel methodological ideas without extensive validation
Primary Subject Area: Segmentation
Secondary Subject Area: Application: Other
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