Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation? - Archive ouverte HAL Access content directly
Conference Papers Year : 2023

Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation?

Abstract

The U-Net model, introduced in 2015, is established as the state-of-the-art architecture for medical image segmentation, along with its variants UNet++, nnU-Net, V-Net, etc. Vision transformers made a breakthrough in the computer vision world in 2021. Since then, many transformer based architectures or hybrid architectures (combining convolutional blocks and transformer blocks) have been proposed for image segmentation, that are challenging the predominance of U-Net. In this paper, we ask the question whether transformers could overtake U-Net for medical image segmentation. We compare SegFormer, one of the most popular transformer architectures for segmentation, to U-Net using three publicly available medical image datasets that include various modalities and organs: segmentation of cardiac structures in ultrasound images from the CAMUS challenge, segmentation of polyp in endoscopy images and segmentation of instrument in colonoscopy images from the MedAI challenge. We compare them in the light of various metrics (segmentation performance, training time) and show that SegFormer can be a true competitor to U-Net and should be carefully considered for future tasks in medical image segmentation.
Fichier principal
Vignette du fichier
miua_sourget-final.pdf (1.05 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-04161509 , version 1 (13-07-2023)

Identifiers

Cite

Théo Sourget, Syed Nouman Hasany, Fabrice Mériaudeau, Caroline Petitjean. Can SegFormer be a True Competitor to U-Net for Medical Image Segmentation?. 27th Conference on Medical Image Understanding and Analysis 2023, Jul 2023, Aberdeen, United Kingdom. ⟨10.1007/978-3-031-48593-0_8⟩. ⟨hal-04161509⟩
243 View
1275 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More