改进U-Net结构，提升腹膜后肿瘤分割精度与效率
A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation
- 融合ViT、Mamba、xLSTM等模块增强U-Net特征提取能力
- xLSTM在保持精度前提下显著降低计算开销
- 适用于医学影像分割，尤其适合资源受限场景
腹膜后区域包含多种罕见的良性和恶性肿瘤，因其罕见性及靠近重要结构，诊断与治疗存在挑战。肿瘤形状不规则，体积估算困难，人工分割耗时。尽管基于U-Net及其变体（含视觉变换器ViT）的自动分割方法表现良好，但计算成本较高。为此，Mamba状态空间模型（SSM）和扩展长短时记忆网络（xLSTM）等架构凭借更低资源消耗实现长程依赖建模，具备高效潜力。本研究评估了融合CNN、ViT、Mamba和xLSTM的U-Net改进方案，在自建CT数据集与公开器官分割数据集上的性能。提出的ViLU-Net模型通过引入Vi-block提升分割效果。结果表明，xLSTM在U-Net框架中兼具高效率与良好性能。代码已开源至GitHub。
原文摘要 · Abstract (English)
The retroperitoneum hosts a variety of tumors, including rare benign and malignant types, which pose diagnostic and treatment challenges due to their infrequency and proximity to vital structures. Estimating tumor volume is difficult due to their irregular shapes, and manual segmentation is time-consuming. Automatic segmentation using U-Net and its variants, incorporating Vision Transformer (ViT) elements, has shown promising results but struggles with high computational demands. To address this, architectures like the Mamba State Space Model (SSM) and Extended Long-Short Term Memory (xLSTM) offer efficient solutions by handling long-range dependencies with lower resource consumption. This study evaluates U-Net enhancements, including CNN, ViT, Mamba, and xLSTM, on a new in-house CT dataset and a public organ segmentation dataset. The proposed ViLU-Net model integrates Vi-blocks for improved segmentation. Results highlight xLSTM's efficiency in the U-Net framework. The code is publicly accessible on GitHub.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。