A novel approach for medical image segmentation combining u-net architectures and active contour models

Medical image segmentation stands as a fundamental health care operation which supports both medical diagnosis and therapeutic development plans. The segmentation of medical images meets difficulties when working on complex anatomical structures in addition to inconsistent image qualities. The proposed system connects U-Net architectures with Active Contour Models to develop more precise segmentation capability. The U-Net identifies key features but ACMs specialize in boundary definition for detailed segmentation outcomes. The diagnosis system received its training and testing on publicly accessible medical image data. The proposed method reached 95.7% segmentation accuracy which proved superior to both standalone U-Net at 92.3% and ACM at 88.5% accuracy. The Dice coefficient reached 0.94 which indicated that boundary identification performed at a high level. Medical image segmentation benefits from the combination of U-Net with ACMs because it provides an effective approach to handle detection boundaries and extract features from medical images. The method demonstrates substantial potential to become applied in healthcare settings.