用Mamba模型解决3D医学图像精细分割的长距离依赖问题
Taming Mambas for Voxel Level 3D Medical Image Segmentation
- 引入Mamba架构替代传统CNN和Transformer处理3D医学体数据
- 在多个公开数据集上实现优于CNN与Transformer的分割精度
- 适合需要高效处理大体积医学影像的临床研究与算法开发人员
近期3D医学图像分割主要依赖卷积神经网络（CNN）和基于Transformer的架构，但前者受限于局部感受野，后者则因高内存需求和数据饥渴难以处理细粒度的3D医学体数据。尽管已有多种Transformer变体尝试降低复杂度，仍缺乏内容感知推理能力。Mamba是一种基于状态空间模型（SSMs）的循环神经网络，在自然语言和基因组学等长序列任务中表现超越Transformer，且保持线性复杂度。本研究首次将Mamba应用于体素级3D医学图像分割，显著提升长距离依赖建模能力，为高精度、低资源消耗的医学影像分析提供新范式。
原文摘要 · Abstract (English)
Recently, the field of 3D medical segmentation has been dominated by deep learning models employing Convolutional Neural Networks (CNNs) and Transformer-based architectures, each with their distinctive strengths and limitations. CNNs are constrained by a local receptive field, whereas transformers are hindered by their substantial memory requirements as well as they data hungriness, making them not ideal for processing 3D medical volumes at a fine-grained level. For these reasons, fully convolutional neural networks, as nnUNet, still dominate the scene when segmenting medical structures in 3D large medical volumes. Despite numerous advancements towards developing transformer variants with subquadratic time and memory complexity, these models still fall short in content-based reasoning. A recent breakthrough is Mamba, a Recurrent Neural Network (RNN) based on State Space Models (SSMs) outperforming Transformers in many long-context tasks (million-length sequences) on famous natural language processing and genomic benchmarks while keeping a linear complexity.
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