SegMamba: Long range Sequential Modeling Mamba For 3D Medical Image Segmentation скачать в хорошем качестве

SegMamba: Long range Sequential Modeling Mamba For 3D Medical Image Segmentation

Paper: https://arxiv.org/pdf/2401.13560 GitHub: https://github.com/ge-xing/SegMamba Abstract. The Transformer architecture has demonstrated remarkable results in 3D medical image segmentation due to its capability of modeling global relationships. However, it poses a significant computational burden when processing high-dimensional medical images. Mamba, as a State Space Model (SSM), has recently emerged as a notable approach for modeling long-range dependencies in sequential data, and has excelled in the field of natural language processing with its remarkable memory efficiency and computational speed. Inspired by this, we devise SegMamba, a novel 3D medical image Segmentation Mamba model, to effectively capture long-range dependencies within whole-volume features at every scale. Our SegMamba outperforms Transformer-based methods in whole volume feature modeling, maintaining high efficiency even at a resolution of 64 ×64×64, where the sequential length is approximately 260k. Moreover, we collect and annotate a novel large-scale dataset (named CRC-500) to facilitate benchmarking evaluation in 3D colorectal cancer (CRC) segmentation. Experimental results on our CRC-500 and two public benchmark datasets further demonstrate the effectiveness and uni versality of our method.