CT to MRI Image Translation Using Paired Medical Image Dataset

However, the inherent high-dimensionality, noise quality, and variability in these images create significant hurdles for conventional image processing methodologies. Conventional image processing approaches may not be scalable nor address the challenges generated by complex imaging data. AI-based segmentation approaches serve as a scalable, automatic approach, particularly models based on deep learning architectures, such as Vision Transformers (ViTs), Convolutional Neural Networks (CNNs), U-Net, etc. These models can automatically extract hierarchical features, qualitatively detect finer details/patterns present in the image, and accurately delineate tumor boundaries in solid organs. Contextual and spatial information are major components in these models that allow for detection of tumors that would otherwise be hidden from the naked eye; this is an important step in diagnosis of cancer at early and more accurate stages. This is particularly exciting and demonstrates a feasible application for applying AD to discovery of early tumors and making other treatment recommendations that are clinically reasonably informed based on existing guidelines. Overall, the results support more interdisciplinary research that combines clinical expertise with medical imaging judgments of artificial intelligence in oncology diagnostic imaging, to improve patient outcomes.