Advancing Semantic Segmentation and Interpretation of 3D Images Through Integrated Deep Learning and Natural Language Processing Techniques

This paper introduces an innovative approach that combines deep learning and natural language processing (NLP) for semantic segmentation and interpretation of three-dimensional images. First, a deep convolutional neural network (CNN) is used to process three-dimensional image data to achieve accurate image segmentation. These networks lay the foundation for subsequent semantic understanding by identifying and distinguishing different objects and structures. Then, a framework based on natural language processing is introduced to combine the segmented images with language descriptions to achieve in-depth explanation of image content. We conduct extensive experiments on multiple 3D image datasets, demonstrating the effectiveness of our approach in semantic segmentation and interpretation of 3D images. Furthermore, we present a case study of the system in real medical applications, highlighting its potential in providing detailed diagnostic information. By comparing the results before and after deep learning and natural language processing, this study highlights the importance and application value of AI technology in the field of medical image processing.