多分辨率特征融合U-Net用于磁共振成像分割
Multi-Resolution Feature Fusion U-Net for Magnetic Resonance Imaging Segmentation
- University of Thessaly（塞萨利大学）
机构由 AI 辅助整理，请以论文原文为准。
中文总结 AI 辅助
提出MRFFU-Net，通过多分辨率特征融合模块增强U-Net，在MRI分割中同时捕获细节与全局信息，并在脑脊液和左心房数据集上超越现有模型。
中文摘要 AI 辅助
医学图像（尤其是MRI扫描）中解剖结构的分割对于临床诊断和监测疾病进展至关重要。虽然深度学习（DL）架构，如U-Net及其扩展，在医学图像分割任务中非常有效，但它们常常难以保留细粒度细节和全局上下文信息。这对于MRI数据分割尤其具有挑战性，因为解剖结构具有不规则的边界以及形状、对比度和尺度的变化。为了应对这一挑战，我们提出了一种新颖的深度学习架构，用于跨不同解剖结构的MRI分割。具体来说，该架构引入了一个名为多分辨率特征融合（MRFF）的模块，该模块可以轻松集成到任何类似U-Net的架构中。MRFF被集成在编码器-解码器结构的所有层级中，并结合注意力机制和跳跃连接，以在多个分辨率下提取特征，使模型能够同时捕获细粒度细节和全局上下文信息。我们在两个公开可用的、针对不同解剖目标的基准MRI数据集上评估了MRFFU-Net：一个用于脊髓MR扫描中的脑脊液（CSF）分割，另一个用于医学分割十项全能（MSD）挑战中的左心房心脏分割。实验结果表明，MRFFU-Net在多个评估指标上优于最先进的模型，证明了其在MRI分割中的有效性。
英文摘要
The segmentation of anatomical structures in medical images and particularly in MRI scans, is essential for clinical diagnosis and monitoring disease progression. While Deep Learning (DL) architectures, such as U-Net and its extensions are very effective in medical image segmentation tasks, they often struggle with preserving fine-grained details and global contextual information. This is especially challenging for MRI data segmentation, where anatomical structures are characterized by irregular boundaries and variations in shape, contrast, and scale. To address this challenge, we propose a novel DL architecture for MRI segmentation across different anatomical structures. Specifically, the architecture introduces a module, named Multi-Resolution Feature Fusion (MRFF), that can be easily integrated into any U-Net-like architecture. The MRFF is integrated in all levels of an encode-decoder structure, along with attention mechanisms and skip connections to extract features at multiple resolutions, enabling the model to capture both fine-grained details and global contextual information. We evaluate the MRFFU-Net on two publicly available benchmark MRI datasets of different anatomical targets; one for Cerebrospinal Fluid (CSF) segmentation in spinal MR scans, and one for left atrium cardiac segmentation, from the Medical Segmentation Decathlon (MSD) challenge. Experimental results indicate that MRFFU-Net outperforms state-of-the-art models across multiple evaluation metrics, demonstrating its effectiveness in MRI segmentation.
