Abstract
The increasing demand for segmentation of lesions in medical images necessitates research on automatic segmentation. Manual segmentation is inefficient due to training time and energy constraints. Deep learning-based image segmentation technology can improve efficiency and aid in diagnosing conditions. This technology provides accurate and detailed data support for clinical medicine, making it a crucial field in medical image processing. This essay introduces image segmentation and its classification, and explains the differences between two segmentation methods, semantic segmentation and instance segmentation and their respective application fields. Additionally, it introduces several well-known deep neural networks for segmenting medical images using deep learning. Regarding the models, this article introduces the structure and characteristics of each model as well as their respective advantages and disadvantages. This essay also introduces examples of deep learning using different deep neural network models to segment specific medical images, including image segmentation based on FCN for the heart and U-Net for the kidneys.
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