Core Insights - GenSeg utilizes AI to generate high-quality medical images and corresponding segmentation labels, significantly reducing the manual labeling burden on medical professionals [1][20] - The framework addresses the critical challenge of dependency on large amounts of high-quality annotated data in medical image semantic segmentation [1][20] Summary by Sections Technology Overview - GenSeg is a three-stage framework that tightly couples data augmentation model optimization with semantic segmentation model training, ensuring that generated samples effectively enhance segmentation model performance [2][10] - It can be applied to various segmentation models, such as UNet and DeepLab, improving their performance in both in-domain and out-of-domain scenarios [4][20] Methodology - The framework consists of two main components: a semantic segmentation model that predicts segmentation masks and a mask-to-image generation model that predicts corresponding images [9] - The training process involves three phases: training the generation model with real image-mask pairs, augmenting real segmentation masks to create synthetic image-mask pairs, and evaluating the segmentation model on a validation set to update the generation model [9][10] Experimental Results - GenSeg demonstrates significant sample efficiency, achieving comparable or superior segmentation performance while drastically reducing the number of training samples required [11][20] - In in-domain experiments, GenSeg-UNet requires only 50 images to achieve a Dice score of approximately 0.6, compared to 600 images for standard UNet, representing a 12-fold reduction in data [13] - In out-of-domain tasks, GenSeg-DeepLab achieves a Jaccard index of 0.67 using only 40 images, while standard DeepLab fails to reach this level with 200 images [13] Comparative Analysis - The end-to-end data generation mechanism of GenSeg outperforms traditional separate training strategies, as evidenced by improved performance metrics in various segmentation tasks [15] - Regardless of the type of generation model used, the end-to-end training strategy consistently outperforms the separate training strategy [17] Generalization and Efficiency - GenSeg exhibits strong generalization capabilities across 11 medical image segmentation tasks and 19 datasets, achieving absolute performance improvements of 10-20% while requiring only 1/8 to 1/20 of the training data compared to existing methods [20]