Automated Lesion Segmentation in Medical Imaging via Integration of nnU-Net Optimization and SAM Approach.

Background: Deep learning has transformed medical imaging by enabling earlier and more accurate disease diagnosis. Lesion and tumor segmentation, essential for analyzing and tracking morphological changes, is commonly done with U-Net variants, though these often lack cross-domain generalization and do not fully leverage foundation models like the Segment Anything Model (SAM), which still requires manual intervention to define the region of interest (ROI). Objectives: To enhance generalization and reduce manual intervention by combining the automatic optimization of nnU-Net with the precision of SAM. Design: Experimental evaluation of a hybrid segmentation framework for lung nodule analysis. Methods: We propose a novel approach integrating the automatic optimization capabilities of nnU-Net for lesion detection with the high-precision segmentation of SAM, eliminating the need for manual intervention by the clinician. The method was evaluated on the LIDC-IDRI dataset, a widely recognized benchmark for lung nodule segmentation. Results: Our approach produces more anatomically coherent segmentations than nnU-Net alone. In many cases, the resulting boundaries more closely reflect true nodule morphology than individual expert annotations, despite high inter-expert variability. Conclusion: The proposed integration of nnU-Net with SAM enables fully automated lesion segmentation without manual intervention. The method improves generalization and accuracy across medical imaging domains, achieving expert-level performance in pulmonary nodule segmentation.