Search papers, labs, and topics across Lattice.
This paper introduces SegMamba-V2, a novel 3D medical image segmentation model that leverages Mamba-based State Space Models to capture long-range dependencies within whole-volume features at multiple scales. The model incorporates a hierarchical scale downsampling strategy to enhance the receptive field and a tri-orientated spatial Mamba block to improve feature representation by modeling dependencies across three orthogonal planes. Evaluated on a newly introduced large-scale CRC-2000 dataset and three other 3D medical image segmentation datasets, SegMamba-V2 significantly outperforms state-of-the-art methods.
Mamba's long-range dependency modeling, previously underutilized in 3D medical imaging, now achieves state-of-the-art segmentation performance thanks to a novel tri-orientated spatial block.
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
