关于U-Net改进在腹膜后肿瘤分割性能的研究

摘要

腹膜后区存在多种肿瘤，包括罕见的良性和恶性类型，由于其罕见性和与重要结构的接近，给诊断和治疗带来挑战。由于肿瘤形状不规则，估计肿瘤体积很困难，而手动分割耗时。使用U-Net及其变体进行自动分割，融合了视觉Transformer（ViT）元素，显示出有希望的结果，但面临高计算需求的挑战。为解决这一问题，像Mamba State Space Model（SSM）和Extended Long-Short Term Memory（xLSTM）这样的架构通过处理长距离依赖关系以及较低资源消耗提供了高效的解决方案。本研究评估了U-Net增强功能，包括CNN、ViT、Mamba和xLSTM，在一组新的内部CT数据集和一个公共器官分割数据集上。提出的ViLU-Net模型整合了Vi块以改善分割效果。结果突显了xLSTM在U-Net框架中的高效性。该代码可在GitHub上公开访问。

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.

关于U-Net改进在腹膜后肿瘤分割性能的研究

A Study on the Performance of U-Net Modifications in Retroperitoneal Tumor Segmentation

摘要

Summary

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