Introduction
Leveraging the nnUNet (no-new UNet) framework for medical image segmentation, we are implementing advanced techniques to divide images into meaningful regions. nnUNet is a powerful tool in deep learning that uses neural networks for accurate and efficient segmentation. This framework based on the U-Net architecture performs well in semantic segmentation, can identify and classify unique objects or regions in images, and greatly reduces the preparatory work for training neural networks.
The advantage of nnUNet is its ability to learn complex patterns and relationships in data, and to achieve high-performance segmentation considering the performance of current graphics processors. Through this method, we can achieve accurate and reliable image segmentation, which is widely used in different fields such as medical image analysis, scene understanding, and object recognition. The versatility and effectiveness of the nnUNet framework play an important role in extracting valuable insights from complex imaging data.