iSegFormer: Interactive Segmentation via Transformers with Application to 3D Knee MR Images

By Qin Liu, Zhenlin Xu, Yining Jiao, Marc Niethammer. 2021

53 citations recorded by the source corpus.

We propose iSegFormer, a memory-efficient transformer that combines a Swin transformer with a lightweight multilayer perceptron (MLP) decoder. With the efficient Swin transformer blocks for hierarchical self-attention and the simple MLP decoder for aggregating both local and global attention, iSegFormer learns powerful representations while achieving high computational efficiencies. Specifically, we apply iSegFormer to interactive 3D medical image segmentation.

Subfields: Computer Vision and Pattern Recognition, Model Architectures, Medical Imaging

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Gedas Bertasius
2. Qin Liu
3. Marc Niethammer
4. Zhenlin Xu
5. Luc Van Gool
6. Jitendra Malik
7. Ming-Hsuan Yang
8. Xiaogang Wang

In Model Architectures

1. Zhenlin Xu
2. Marc Niethammer
3. Qin Liu
4. Gedas Bertasius

In Medical Imaging

1. Zhenlin Xu
2. Marc Niethammer
3. Qin Liu

Citations in the corpus

• SimpleClick: Interactive Image Segmentation with Simple Vision Transformers (2022, methodology): “For the former direction, different hierarchical backbones, including both ConvNets [30,43] and ViTs [10,33], have been developed for interactive segmentation.”