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

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.