A Novel Optimization of the ResU-Net Model for High Precision Multi-Organ Segmentation

The precise identification and segmentation are indispensable for effective therapy planning and complete surgi cal management in medical imaging. However, conventional segmentation methods recurrently encounter chal lenges due to the intrinsic complexity of anatomical structures, variability in organ structure, and inconsistency across diverse imaging modalities. This study employs an optimized hybrid deep learning approach that inte grates ResNet50 with an attention-augmented UNet architecture to improve segmentation accuracy and organ localization in medical imaging. The ResNet50 model uses an encoder that focuses on deep feature extraction, whereas the UNet serves as the decoder and is enhanced with an attention mechanism. They are both integrated to enhance the model’s efficiency by capturing both the global framework and local spatial details through a hy brid structure and skip connections, which improve segmentation performance. The proposed model was trained on a large-scale multi-organ dataset of high-resolution MRI to ensure robustness. In addition, the dataset was augmented and regularized to stabilize the model. The major performance metrics, namely Intersection over Union (IoU) and Dice coefficient, indicate that the proposed scheme achieves a high segmentation precision of up to 98.41%. These outcomes indicate that the model is highly feasible for deployment in clinical workflows to improve multi-organ detection with precise performance.