bibtext: @InProceedings{ Xin_SegMamba_MICCAI2024,
                   author = { Xing, Zhaohu and Ye, Tian and Yang, Yijun and Liu, Guang and Zhu, Lei },
                   title = { { SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15008 },
                   month = {October},
                   pages = { pending },
                }
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 \textbf{SegMamba}, a novel 3D medical image \textbf{Seg}mentation \textbf{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 {$64\times 64\times 64$}, 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 universality of our method.
title: SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image Segmentation
authors: Xing, Zhaohu Ye, Tian Yang, Yijun Liu, Guang Zhu, Lei