Kidney Stone Segmentation from Computed Tomography Images Using Computer Vision Techniques

Objective Urolithiasis is a common condition in urological practice, and accurate imaging is essential for proper diagnosis and management. Computed tomography (CT) is widely accepted as the gold standard for evaluating kidney stones due to its high sensitivity in assessing stone location, size, and morphology. However, manual interpretation of CT images is time-consuming and subject to observer-dependent variability. This study aimed to develop a deep learning–based model for automatic kidney stone segmentation and size estimation from CT images and to integrate this model into a web-based clinical decision support system. Methods An open-access dataset consisting of 3,584 CT images and corresponding kidney stone segmentation masks was used. The dataset was divided into training (70%), validation (20%), and test (10%) subsets. Image preprocessing and model development were performed using Python and the TensorFlow framework. A U-Net + + architecture was employed for kidney stone segmentation. Model training was conducted in the Google Colab environment. Stone size estimation was performed using the equivalent circular diameter method based on the segmented stone area. Model performance was evaluated using Precision, Recall, Precision–Recall Area Under the Curve (PR-AUC), Intersection over Union (IoU), and Dice/F1-score metrics. The trained model was additionally deployed via a web-based interface to demonstrate potential clinical applicability. Results On the test dataset, the proposed model achieved a PR-AUC of 90.58%. The Dice/F1-score and IoU values were 82.10% and 69.63%, respectively. Precision and Recall were 76.66% and 88.36%. Qualitative evaluation demonstrated accurate localization and segmentation of kidney stones, including small calculi. Conclusion This study presents an effective deep learning–based approach for automated kidney stone segmentation and size estimation from CT images. The favorable quantitative results and consistent visual performance suggest that the proposed system may support clinical decision-making by reducing workload and improving diagnostic consistency in routine urological practice.