D-Net：用于体积医学图像分割的动态大核与动态特征融合
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
AI总结：
本文提出D-Net，通过动态大核与动态特征融合模块增强层次化Transformer，自适应捕捉多尺度特征并利用全局上下文，在腹部多器官和脑肿瘤分割中超越现有方法。
摘要：
层次化Transformer因其较大的感受野以及有效利用全局长程上下文信息的能力，在医学图像分割中取得了显著成功。卷积神经网络（CNNs）也可以通过使用大核来获得较大的感受野，从而以更少的模型参数实现具有竞争力的性能。然而，由于采用固定大小的卷积核，结合了大卷积核的CNNs在自适应捕捉形状和大小变化较大的器官的多尺度特征方面仍然受到限制。此外，它们无法高效利用全局上下文信息。为了解决这些局限性，我们提出了动态大核（DLK）和动态特征融合（DFF）模块。DLK模块采用多个具有不同核大小和膨胀率的大核来捕捉多尺度特征。随后，利用动态选择机制基于全局信息自适应地突出最重要的空间特征。此外，提出了DFF模块，以根据其全局信息自适应地融合多尺度局部特征图。我们将DLK和DFF集成到层次化Transformer架构中，开发出一种新颖的架构，称为D-Net。D-Net能够有效利用多尺度大感受野并自适应地利用全局上下文信息。大量实验结果表明，D-Net在两个体积分割任务中优于其他最先进模型，包括腹部多器官分割和多模态脑肿瘤分割。我们的代码可在https://github.com/sotiraslab/DLK获取。
英文摘要：
Hierarchical transformers have achieved significant success in medical image segmentation due to their large receptive field and capabilities of effectively leveraging global long-range contextual information. Convolutional neural networks (CNNs) can also deliver a large receptive field by using large kernels, enabling them to achieve competitive performance with fewer model parameters. However, CNNs incorporated with large convolutional kernels remain constrained in adaptively capturing multi-scale features from organs with large variations in shape and size due to the employment of fixed-sized kernels. Additionally, they are unable to utilize global contextual information efficiently. To address these limitations, we propose Dynamic Large Kernel (DLK) and Dynamic Feature Fusion (DFF) modules. The DLK module employs multiple large kernels with varying kernel sizes and dilation rates to capture multi-scale features. Subsequently, a dynamic selection mechanism is utilized to adaptively highlight the most important spatial features based on global information. Additionally, the DFF module is proposed to adaptively fuse multi-scale local feature maps based on their global information. We integrate DLK and DFF in a hierarchical transformer architecture to develop a novel architecture, termed D-Net. D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information. Extensive experimental results demonstrate that D-Net outperforms other state-of-the-art models in the two volumetric segmentation tasks, including abdominal multi-organ segmentation and multi-modality brain tumor segmentation. Our code is available at https://github.com/sotiraslab/DLK.
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。
