The introduction of medical image enhancement and accurate anatomical distinction detection methods based on deep learning has become one of the revolutionary technologies of modern-day medical imaging. The correct visualization of the anatomical structures is essential in diagnosis, treatment planning, and to monitor the disease progression, but the conventional visualization strategies usually exhibit low contrast, noise, artifacts, and interimagery differences among different imaging modalities. This chapter offers an in-depth discussion of deep learning methods that can overcome these problems, such as convolutional neural networks (CNNs), encoder-decoder models, attention systems, generative networks, and new implicit representations such as Neural Radiance Fields. The main image refinement techniques mentioned are denoising, super-resolution, contrast-enhancement, and multi-modal fusion, which are all geared towards enhancing structural clarity and edge visibility. The following methods of boundary detection are considered based on anatomical accuracy and clinical relevance: semantic and instance segmentation, attention-based refinement, hybrid CNN-Transformer models, and graph-based methods. Also introduced in the chapter are uses in tumor delineation and organ segmentation, surgical planning and longitudinal disease monitoring, which is backed by experimental analysis using benchmark datasets. The ethical implications, data constraints, and the problem of clinical validation are mentioned to focus on the responsible use of AI. Combining image enhancement using deep learning with accurate boundary detection, the chapter highlights the possibilities of intelligent imaging systems of the next generation to enhance accurate diagnostic results, treatment planning, and personal medical care.