Xing Zhaohu, Ye Tian, Yang Yijun, Cai Du, Gai Baowen, Wu Xiao-Jian, Gao Feng, Zhu Lei
IEEE Trans Med Imaging. 2025 Jul 18;PP. doi: 10.1109/TMI.2025.3589797.
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. Although a substantial amount of Mamba-based research has focused on natural language and 2D image processing, few studies explore the capability of Mamba on 3D medical images. In this paper, we propose SegMamba-V2, a novel 3D medical image segmentation model, to effectively capture long-range dependencies within whole-volume features at each scale. To achieve this goal, we first devise a hierarchical scale downsampling strategy to enhance the receptive field and mitigate information loss during downsampling. Furthermore, we design a novel tri-orientated spatial Mamba block that extends the global dependency modeling process from one plane to three orthogonal planes to improve feature representation capability. Moreover, we collect and annotate a large-scale dataset (named CRC-2000) with fine-grained categories to facilitate benchmarking evaluation in 3D colorectal cancer (CRC) segmentation. We evaluate the effectiveness of our SegMamba-V2 on CRC-2000 and three other large-scale 3D medical image segmentation datasets, covering various modalities, organs, and segmentation targets. Experimental results demonstrate that our Segmamba-V2 outperforms state-of-the-art methods by a significant margin, which indicates the universality and effectiveness of the proposed model on 3D medical image segmentation tasks. The code for SegMamba-V2 is publicly available at: https://github.com/ge-xing/SegMamba-V2.
由于具有全局关系建模能力，Transformer架构在3D医学图像分割中取得了显著成果。然而，在处理高维医学图像时，它带来了巨大的计算负担。作为一种状态空间模型（SSM），Mamba最近成为了一种在序列数据中建模长程依赖关系的显著方法。尽管大量基于Mamba的研究集中在自然语言和2D图像处理上，但很少有研究探索Mamba在3D医学图像上的能力。在本文中，我们提出了一种新颖的3D医学图像分割模型SegMamba-V2，以有效捕捉每个尺度下全卷特征内的长程依赖关系。为实现这一目标，我们首先设计了一种分层尺度下采样策略，以增强感受野并减轻下采样过程中的信息损失。此外，我们设计了一种新颖的三向空间Mamba块，将全局依赖关系建模过程从一个平面扩展到三个正交平面，以提高特征表示能力。此外，我们收集并注释了一个具有细粒度类别的大规模数据集（名为CRC-2000），以促进3D结直肠癌（CRC）分割中的基准评估。我们在CRC-2000和其他三个大规模3D医学图像分割数据集上评估了SegMamba-V2的有效性，这些数据集涵盖了各种模态、器官和分割目标。实验结果表明，我们的Segmamba-V2显著优于现有方法，这表明所提出的模型在3D医学图像分割任务中的通用性和有效性。SegMamba-V2的代码可在以下网址公开获取：https://github.com/ge-xing/SegMamba-V2。
