Details

Autor(en) / Beteiligte

• Wang, Jing
• Wang, Shuyu
• Liang, Wei

Titel

METrans: Multi‐encoder transformer for ischemic stroke segmentation

Ist Teil von

• Electronics letters, 2022-04, Vol.58 (9), p.340-342

Ort / Verlag

Stevenage: John Wiley & Sons, Inc

Erscheinungsjahr

2022

Quelle

Wiley Online Library

Beschreibungen/Notizen

• Ischemic stroke is the most common brain disease. Segmentation of the stroke lesion from a medical scan is vital to plan the surgical procedure. The purpose of this work is to develop an efficient network based on self‐attention and spatial‐channel attention mechanisms. In this letter, a novel multi‐encoder transformer (METrans) is proposed, which overcomes the inability of U‐Nets to model long‐range contextual interactions. Different from traditional segmentation methods, four encoders with different scales are explored to extract the multi‐scale features. Then, the feature maps are fed into a transformer for global feature modelling. The proposed methodology is tested on ATLAS, ISLES 2015, and ISLES 2018 datasets. The extensive experimental results suggest that METrans achieves consistent improvements over the state‐of‐the‐art for segmentation tasks.