Abstract
The advent of deep learning enabled the extraction of complex feature representations from medical imaging data, which was considered impossible to be achieved with standard computer learning. The applications of deep learning in the field of medical image analysis a ord significant results. A key feature of deep learning techniques is their ability to automatically learn task-specific feature representations and extract relevant features without human intervention. Various deep learning models, including CNN, AlexNet, ResNet, DenseNet and U-Net were developed for medical image analysis. Among these models, U-Net is a popular model, used for medical image segmentation. The present article provides a comprehensive review of the deep learning segmentation models, which use U-Net and its variants, applied in the domain of medical image segmentation, specifically tailored to medical imaging modalities, such as ultrasound and MRI, along with respective pros and cons in the field of image segmentation. The analysis reveals that the performance of di erent U-Net variants varies significantly based on imaging modality and segmentation complexity.