PDF(Pubmed)
Abstract：
Automated segmentation of multiple organs and tumors from 3D medical images such as magnetic resonance imaging (MRI) and computed tomography (CT) scans using deep learning methods can aid in diagnosing and treating cancer. However, organs often overlap and are complexly connected, characterized by extensive anatomical variation and low contrast. In addition, the diversity of tumor shape, location, and appearance, coupled with the dominance of background voxels, makes accurate 3D medical image segmentation difficult. In this paper, a novel 3D large-kernel (LK) attention module is proposed to address these problems to achieve accurate multi-organ segmentation and tumor segmentation. The advantages of biologically inspired self-attention and convolution are combined in the proposed LK attention module, including local contextual information, long-range dependencies, and channel adaptation. The module also decomposes the LK convolution to optimize the computational cost and can be easily incorporated into CNNs such as U-Net. Comprehensive ablation experiments demonstrated the feasibility of convolutional decomposition and explored the most efficient and effective network design. Among them, the best Mid-type 3D LK attention-based U-Net network was evaluated on CT-ORG and BraTS 2020 datasets, achieving state-of-the-art segmentation performance when compared to avant-garde CNN and Transformer-based methods for medical image segmentation. The performance improvement due to the proposed 3D LK attention module was statistically validated.
摘要：
使用深度学习方法从3D医学图像（如磁共振成像（MRI）和计算机断层扫描（CT）扫描）中自动分割多个器官和肿瘤，可以帮助诊断和治疗癌症。然而,器官经常重叠和复杂的连接，其特点是广泛的解剖变异和低对比度。此外,肿瘤形状的多样性，location,和外观，再加上背景体素的优势，使得三维医学图像的精确分割变得困难。在本文中,提出了一种新颖的3D大核（LK）注意模块来解决这些问题，以实现准确的多器官分割和肿瘤分割。在提出的LK注意力模块中结合了生物启发的自我注意力和卷积的优点，包括本地上下文信息，远程依赖，和频道适应。该模块还分解LK卷积以优化计算成本,并且可以容易地并入诸如U-Net的CNN中。综合消融实验证明了卷积分解的可行性，并探索了最有效和最有效的网络设计。其中,在CT-ORG和BraTS2020数据集上评估了最佳的中型3DLK基于注意力的U-Net网络，与前卫CNN和基于Transformer的医学图像分割方法相比，实现了最先进的分割性能。对由于所提出的3DLK注意力模块而产生的性能改进进行了统计验证。
