Transformer architecture has emerged to be successful in a number of natural
language processing tasks. However, its applications to medical vision remain
largely unexplored. In this study, we present UTNet, a simple yet powerful
hybrid Transformer architecture that integrates self-attention into a
convolutional neural network for enhancing medical image segmentation. UTNet
applies self-attention modules in both encoder and decoder for capturing
long-range dependency at different scales with minimal overhead. To this end,
we propose an efficient self-attention mechanism along with relative position
encoding that reduces the complexity of self-attention operation significantly
from $O(n^2)$ to approximate $O(n)$. A new self-attention decoder is also
proposed to recover fine-grained details from the skipped connections in the
encoder. Our approach addresses the dilemma that Transformer requires huge
amounts of data to learn vision inductive bias. Our hybrid layer design allows
the initialization of Transformer into convolutional networks without a need of
pre-training. We have evaluated UTNet on the multi-label, multi-vendor cardiac
magnetic resonance imaging cohort. UTNet demonstrates superior segmentation
performance and robustness against the state-of-the-art approaches, holding the
promise to generalize well on other medical image segmentations.

本研究提出 UTNet，这是一种简单而强大的混合 Transformer 体系结构，它将 self-attention 集成到卷积神经网络中，以增强医学图像分割，通过在编码器和解码器中应用 self-attention 模块来捕捉不同尺度的长程依赖关系，并提出了一种有效的 self-attention 机制与相对位置编码，从而将 self-attention 操作的复杂度从 $O (n^2)$ 降低到约 $O (n)$。在多标签、多厂商的心脏磁共振成像队列上对 UTNet 进行了评估，所示分割性能优异，对抗最先进的方法表现出很好的鲁棒性，有望在其他医学图像分割中具有广泛的应用前景。