Medical image segmentation plays a crucial role in various healthcare
applications, enabling accurate diagnosis, treatment planning, and disease
monitoring. In recent years, Vision Transformers (ViTs) have emerged as a
promising technique for addressing the challenges in medical image
segmentation. In medical images, structures are usually highly interconnected
and globally distributed. ViTs utilize their multi-scale attention mechanism to
model the long-range relationships in the images. However, they do lack
image-related inductive bias and translational invariance, potentially
impacting their performance. Recently, researchers have come up with various
ViT-based approaches that incorporate CNNs in their architectures, known as
Hybrid Vision Transformers (HVTs) to capture local correlation in addition to
the global information in the images. This survey paper provides a detailed
review of the recent advancements in ViTs and HVTs for medical image
segmentation. Along with the categorization of ViT and HVT-based medical image
segmentation approaches we also present a detailed overview of their real-time
applications in several medical image modalities. This survey may serve as a
valuable resource for researchers, healthcare practitioners, and students in
understanding the state-of-the-art approaches for ViT-based medical image
segmentation.

医学图像分割是各种医疗应用中至关重要的一环，能够实现准确的诊断、治疗规划和疾病监测。最近，视觉变换器（ViTs）作为一种有望解决医学图像分割挑战的技术逐渐崭露头角。本综述论文对于医学图像分割中 ViTs 和混合视觉变换器（HVTs）的最新发展进行了详细的回顾。除了对 ViT 和 HVT 的分类外，还详细介绍了它们在几种医学图像模态中的实时应用。这篇综述可以作为研究人员、医疗从业者和学生了解 ViT 基于医学图像分割的最新方法的有价值的资源。