Convolutional Neural Networks (CNNs) have exhibited strong performance in medical image segmentation tasks by capturing high-level (local) information, such as edges and textures. However, due to the limited field of view of convolution kernel, it is hard for CNNs to fully represent global information. Recently, transformers have shown good performance for medical image segmentation due to their ability to better model long-range dependencies. Nevertheless, transformers struggle to capture high-level spatial features as effectively as CNNs. A good segmentation model should learn a better representation from local and global features to be both precise and semantically accurate. In our previous work, we proposed CATS, which is a U-shaped segmentation network augmented with transformer encoder. In this work, we further extend this model and propose CATS v2 with hybrid encoders. Specifically, hybrid encoders consist of a CNN-based encoder path paralleled to a transformer path with a shifted window, which better leverage both local and global information to produce robust 3D medical image segmentation. We fuse the information from the convolutional encoder and the transformer at the skip connections of different resolutions to form the final segmentation. The proposed method is evaluated on three public challenge datasets: Beyond the Cranial Vault (BTCV), Cross-Modality Domain Adaptation (CrossMoDA) and task 5 of Medical Segmentation Decathlon (MSD-5), to segment abdominal organs, vestibular schwannoma (VS) and prostate, respectively. Compared with the state-of-the-art methods, our approach demonstrates superior performance in terms of higher Dice scores. Our code is publicly available at https://github.com/MedICL-VU/CATS.

翻译：卷积神经网络通过捕获边缘和纹理等高层（局部）信息，在医学图像分割任务中展现出强劲性能。然而，由于卷积核的视野受限，CNN难以完整表征全局信息。近年来，Transformer凭借其建模长距离依赖关系的优势，在医学图像分割中表现优异，但其捕获高层空间特征的能力仍不及CNN。理想的分割模型应兼顾局部与全局特征的学习能力，以实现精确且语义准确的分割。本团队此前提出的CATS模型是一种融合Transformer编码器的U形分割网络。在此基础上，本研究进一步扩展提出具有混合编码器的CATS v2：混合编码器由基于CNN的编码路径与基于移位窗口的Transformer路径并行构成，从而更好地结合局部与全局信息以实现鲁棒的3D医学图像分割。我们将卷积编码器与Transformer的信息在不同分辨率的跳跃连接处进行融合，生成最终分割结果。该方法在三个公开挑战数据集（颅穹窿外器官分割数据集BTCV、跨模态域适应数据集CrossMoDA及医学分割十项全能任务5 MSD-5）上分别针对腹部器官、前庭神经鞘瘤和前列腺进行验证。与现有最优方法相比，本方法在Dice评分上展现出更优性能。代码已公开于https://github.com/MedICL-VU/CATS。