  Medical image segmentation plays a crucial role in various healthcare
applications, enabling accurate diagnosis, treatment planning, and disease
monitoring. Traditionally, convolutional neural networks (CNNs) dominated this
domain, excelling at local feature extraction. However, their limitations in
capturing long-range dependencies across image regions pose challenges for
segmenting complex, interconnected structures often encountered in medical
data. In recent years, Vision Transformers (ViTs) have emerged as a promising
technique for addressing the challenges in medical image segmentation. Their
multi-scale attention mechanism enables effective modeling of long-range
dependencies between distant structures, crucial for segmenting organs or
lesions spanning the image. Additionally, ViTs' ability to discern subtle
pattern heterogeneity allows for the precise delineation of intricate
boundaries and edges, a critical aspect of accurate medical image segmentation.
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.
