Abstract
BACKGROUND: Although convolutional neural network (CNN)-based methods have been widely used in medical image analysis and have achieved great success in many medical segmentation tasks, these methods suffer from various imbalance problems, which reduce the accuracy and validity of segmentation results. METHODS: We proposed two simple but effective sample balancing methods, positive-negative subset selection (PNSS) and hard-easy subset selection (HESS) for foreground-to-background imbalance and hard-to-easy imbalance problems in medical segmentation tasks. The PNSS method gradually reduces negative-easy slices to enhance the contribution of positive pixels, and the HESS method enhances the iteration of hard slices to assist the model in paying greater attention to the feature extraction of hard samples. RESULTS: The proposed methods greatly improved the segmentation accuracy of the worst case (samples with the worst segmentation results) on the public National Institutes of Health (NIH) clinical center pancreatic segmentation dataset, and the minimum dice similarity coefficient (DSC) was improved by nearly 5%. Furthermore, performance gains were also observed with the proposed methods in liver segmentation (the minimum DSC increased from 75.03% to 84.29%), liver tumor segmentation (the minimum DSC increased from 20.92% to 35.73%), and brain tumor segmentation (the minimum DSC increased from 21.97% to 30.38%) on different neural networks. These results indicate that the proposed methods are effective and robust. CONCLUSIONS: Our proposed method can effectively alleviate foreground-to-background imbalance and hard-to-easy imbalance problems, and can improve segmentation accuracy, especially for the worst case, which guarantees the reliability of the proposed methods in clinical applications.