Convolutional Neural Networks (CNNs)  and Vision Transformers (ViT)  have been pivotal in biomedical image segmentation. Yet, their ability to manage long-range dependencies remains constrained by inherent locality and computational overhead.
To overcome these challenges, in this technical report, we first propose xLSTM-UNet, a UNet structured deep learning neural network that leverages Vision-LSTM (xLSTM)  as its backbone for medical image segmentation. xLSTM has recently been proposed as the successor of Long Short-Term Memory (LSTM)  networks and has demonstrated superior performance compared to Transformers and State Space Models (SSMs)  like Mamba in Neural Language Processing (NLP)  and image classification (as demonstrated in Vision-LSTM, or ViL implementation) . Here, xLSTM-UNet we designed to extend the success in the biomedical image segmentation domain. By integrating the local feature extraction strengths of convolutional layers with the long-range dependency-capturing abilities of xLSTM, xLSTM-UNet offers a robust solution for comprehensive image analysis. We validate the efficacy of xLSTM-UNet through experiments. Our findings demonstrate that xLSTM-UNet consistently surpasses the performance of leading CNN-based, Transformer-based, and Mamba-based segmentation networks in multiple datasets in biomedical segmentation including organs in abdomen MRI, instruments in endoscopic images, and cells in microscopic images. With comprehensive experiments performed, this technical report highlights the potential of xLSTM-based architectures in advancing biomedical image analysis in both 2D and 3D. The code, models, and datasets are publicly available athttp://tianrun-chen.github.io/xLSTM-Unet/.