利用局部自注意力和全局体积混合改进边界区域的3D医学图像分割
Improving 3D Medical Image Segmentation at Boundary Regions using Local Self-attention and Global Volume Mixing
AI总结：
本文提出一种结合局部体积自注意力与全局体积MLP-mixer的分层编码器-解码器框架，通过显式建模局部和全局依赖改善3D医学图像边界分割，在多个数据集上取得优于现有方法的性能。
摘要：
体积医学图像分割是医学图像分析中的一个基本问题，其目标是以体素级精度准确分类给定的3D体积医学图像。在这项工作中，我们提出了一种新颖的基于分层编码器-解码器的框架，该框架致力于显式捕获体积3D医学图像分割的局部和全局依赖关系。所提出的框架利用基于局部体积的自注意力在高分辨率下编码局部依赖关系，并引入一种新颖的体积MLP-mixer在低分辨率特征表示中捕获全局依赖关系。所提出的体积MLP-mixer能够学习体积特征表示之间更好的关联。这些显式的局部和全局特征表示有助于更好地学习器官的形状边界特征。在三个不同数据集上的大量实验表明，与最先进的方法相比，所提出的方法取得了良好的性能。在具有挑战性的Synapse多器官数据集上，所提出的方法在HD95评估指标上比最先进的方法绝对提升了3.82%，同时在MSD肝脏和胰腺肿瘤数据集上也表现出类似的改进模式。我们还通过将2D计算机视觉文献中最近的架构设计选择适配到3D医学图像分割问题，提供了详细的比较。最后，我们在训练数据有限的ZebraFish 3D细胞膜数据集上的实验表明，所提出的vMixer模型在具有挑战性的3D细胞实例分割任务上具有优越的迁移学习能力，其中准确的边界预测在区分单个细胞实例中起着至关重要的作用。
英文摘要：
Volumetric medical image segmentation is a fundamental problem in medical image analysis where the objective is to accurately classify a given 3D volumetric medical image with voxel-level precision. In this work, we propose a novel hierarchical encoder-decoder-based framework that strives to explicitly capture the local and global dependencies for volumetric 3D medical image segmentation. The proposed framework exploits local volume-based self-attention to encode the local dependencies at high resolution and introduces a novel volumetric MLP-mixer to capture the global dependencies at low-resolution feature representations, respectively. The proposed volumetric MLP-mixer learns better associations among volumetric feature representations. These explicit local and global feature representations contribute to better learning of the shape-boundary characteristics of the organs. Extensive experiments on three different datasets reveal that the proposed method achieves favorable performance compared to state-of-the-art approaches. On the challenging Synapse Multi-organ dataset, the proposed method achieves an absolute 3.82\% gain over the state-of-the-art approaches in terms of HD95 evaluation metrics {while a similar improvement pattern is exhibited in MSD Liver and Pancreas tumor datasets}. We also provide a detailed comparison between recent architectural design choices in the 2D computer vision literature by adapting them for the problem of 3D medical image segmentation. Finally, our experiments on the ZebraFish 3D cell membrane dataset having limited training data demonstrate the superior transfer learning capabilities of the proposed vMixer model on the challenging 3D cell instance segmentation task, where accurate boundary prediction plays a vital role in distinguishing individual cell instances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
