用智能Transformer+密集U-Net自动去除大鼠功能MRI颅骨，精度超98%。
SST-DUNet: Automated preclinical functional MRI skull stripping using Smart Swin Transformer and Dense UNet
- 结合智能Swin Transformer与密集U-Net，捕捉脑结构关键特征。
- 在三个大鼠fMRI数据集上Dice分数达98.65%、97.86%、98.04%。
- 适合需要高效处理低分辨率预临床fMRI的神经影像研究者。
颅骨剥离是磁共振成像（MRI）流程中的常见预处理步骤，尤其在功能MRI（fMRI）中常需人工操作，耗时且依赖人员。自动化过程对预临床数据极具挑战，因脑部形态、分辨率和组织对比度差异大。现有方法难以应对预临床fMRI数据的低分辨率与可变切片尺寸。本文提出SST-DUNet模型，融合基于Smart Swin Transformer（SST）的特征提取器与密集U-Net架构，实现自动颅骨剥离。SST中的智能移位窗口多头自注意力模块替代原版Swin Transformer的掩码机制，有效学习通道特异性特征并聚焦脑结构相关依赖。为缓解预临床数据的类别不平衡问题，采用焦点损失与Dice损失联合优化。模型在大鼠fMRI图像上训练，并在三个自有数据集上评估，获得98.65%、97.86%、98.04%的Dice相似性得分。使用SST-DUNet自动剥离后所得fMRI结果，与人工剥离结果在种子分析和独立成分分析中高度一致，表明其可有效替代人工脑提取。
原文摘要 · Abstract (English)
Skull stripping is a common preprocessing step that is often performed manually in Magnetic Resonance Imaging (MRI) pipelines, including functional MRI (fMRI). This manual process is time-consuming and operator dependent. Automating this process is challenging for preclinical data due to variations in brain geometry, resolution, and tissue contrast. While existing methods for MRI skull stripping exist, they often struggle with the low resolution and varying slice sizes in preclinical fMRI data. This study proposes a novel method called SST-DUNet, that integrates a dense UNet-based architecture with a feature extractor based on Smart Swin Transformer (SST) for fMRI skull stripping. The Smart Shifted Window Multi-Head Self-Attention (SSW-MSA) module in SST is adapted to replace the mask-based module in the Swin Transformer (ST), enabling the learning of distinct channel-wise features while focusing on relevant dependencies within brain structures. This modification allows the model to better handle the complexities of fMRI skull stripping, such as low resolution and variable slice sizes. To address the issue of class imbalance in preclinical data, a combined loss function using Focal and Dice loss is utilized. The model was trained on rat fMRI images and evaluated across three in-house datasets with a Dice similarity score of 98.65%, 97.86%, and 98.04%. The fMRI results obtained through automatic skull stripping using the SST-DUNet model closely align with those from manual skull stripping for both seed-based and independent component analyses. These results indicate that the SST-DUNet can effectively substitute manual brain extraction in rat fMRI analysis.
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