MSDCA-UNet medical image segmentation system orchestrates interactive 3D MRI analysis through a GUI-driven workflow. The pipeline loads multimodal NIfTI volumetric data, applies Z-score normalization preprocessing, executes model inference on GPU, and renders predicted segmentation masks in real-time. This architecture demonstrates best practices for clinical-grade deep learning applications: efficient memory management, interactive slice-by-slice navigation, and immediate visual feedback for radiologists. Fork this diagram on Diagrams.so to customize preprocessing steps, integrate alternative segmentation models, or adapt the rendering pipeline for your imaging modality. The feedback loop between user interaction and slice rendering enables iterative refinement workflows common in diagnostic imaging tools.