Automatic liver segmentation using U-Net deep learning architecture for additive manufacturing

Medical image analysis requires liver segmentation for liver disease detection and treatment. Deep learning approaches, particularly liver segmentation, have demonstrated astounding effectiveness in a variety of medical imaging applications. Using the U-Net architecture, a well-liked and successful deep learning model for semantic segmentation, a liver segmentation approach is suggested in this study. This approach uses 3D abdominal CT images with liver regions identified. The U-Net model collects local and global contextual data via skip links and an encoder-decoder network. Supervised learning and data augmentation are used to develop the network's generalization ability. Intensity normalization, voxel resampling, and image cropping were used to enhance liver segmentation by improving input data quality and consistency. Post-processing approaches like linked component analysis and morphology improved segmentation results and eliminated false positives. A separate test dataset and conventional assessment criteria as DSC, sensitivity, and specificity were employed to evaluate our liver segmentation approach. A Dice score of 0.9287 indicates a 92.87% overlap between the sets. This is a good result since the segmentation or comparison approach identified and aligned the matching regions in the sets. Train dice loss, train metric dice, test dice loss, test metric dice and mean dice are found to be 0.0223, 0.9733, 0.289, 0.782, and 0.9287 respectively. Lab results reveal that the current liver segmentation approach is accurate and resilient. Comparing present strategy to other cutting-edge liver segmentation methods shows its competitiveness. In conclusion, this study proposes a liver segmentation method based on the U-Net architecture that successfully tackles the difficulties in precisely distinguishing the liver from abdominal CT scans. The suggested method has produced encouraging results, demonstrating its potential for clinical uses in the diagnosis of liver disease, surgical planning, and therapy monitoring.