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Abstract #1209

SegFormer for Precise Quantification of Lung Ventilation Defects in Hyperpolarized Gas Lung MRI

Ramtin Babaeipour1, Ryan Zhu2, Harsh Patel2, Matthew S Fox2,3, and Alexei Ouriadov1,2,3
1School of Biomedical Engineering, Faculty of Engineering, The University of Western Ontario, London, ON, Canada, 2Department of Physics and Astronomy, The University of Western Ontario, London, ON, Canada, 3Lawson Health Research Institute, London, ON, Canada

Synopsis

Keywords: Analysis/Processing, Hyperpolarized MR (Gas), Deep Learning; Magnetic Resonance Imaging (MRI); Hyperpolarized Gas MRI; Segmentation; Ventilation Defect; Chronic Obstructive Pulmonary Disease (COPD); Lung Imaging

Motivation: Current methods for quantifying lung ventilation defects using hyperpolarized gas MRI are effective but time-consuming. Deep Learning offers potential enhancements in image segmentation, with Vision Transformers (ViTs) emerging as notable alternatives to traditional CNNs.

Goal(s): The study aims to assess SegFormer's capability for automating the segmentation and quantification of ventilation defects in hyperpolarized gas MRI, comparing its efficiency and accuracy against traditional methods.

Approach: Utilizing a dataset from 56 study participants, the study adopted the SegFormer architecture for segmenting MRI slices.

Results: SegFormer, especially with ImageNet pretraining, surpassed CNN-based techniques in segmentation. Specifically, the MiT-B2 configuration of SegFormer showcased exceptional efficacy and efficiency.

Impact: SegFormer's efficiency in hyperpolarized gas MRI enhances future clinical decision-making with swift and precise segmentation. Its superiority may inspire broader adoption and further exploration into Vision Transformers' potential in medical imaging.

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Abstract

Keywords