Coarse-to-fine tuning knowledgeable system for boundary delineation in medical images

Medical ultrasound image segmentation is crucial to the clinical diagnosis of planning for medical diseases. However, this task is challenging because of the missing/ambiguous edges and inhomogeneous intensity distribution of organs usually noted in ultrasound images. In this study, we devised a new coarse-to-refined architecture for different organ segmentation tasks. Our presented model has four merits: first, our work exploits the benefit of artificial intelligence algorithm to intelligently locate the target area and the feature of the principal curve to intelligently approach the center of data points in the refinement step. Second, we designed an enhanced polygon tracking model to increase our algorithm’s accuracy and efficiency. Third, to ensure population diversity and achieve optimal model initialization, we improved the traditional quantum evolution network both the numerous operator and global optimum search algorithm. Fourth, we devised an interpretable mathematical mapping function to smoothen the contour of the region of interest, which is expressed through the neural network model parameters. Outcomes on different types of datasets indicate that our developed model achieves excellent segmentation capability, yielding an average Dice similarity coefficient, Jaccard similarity coefficient, and accuracy of 94.1% ± 3.9%, 92.4% ± 4.7%, and 93.6% ± 4.1%, respectively. Graphical abstract