Generative Medical Segmentation

Rapid advancements in medical image segmentation performance have been significantly driven by the development of Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). These models follow discriminative pixel-wise classification learning paradigm and often have limited ability to generalize across diverse medical imaging datasets. In this manuscript, we introduce Generative Medical Segmentation (GMS), a novel generative approach to perform image segmentation. GMS employs a robust pre-trained vision foundation model to extract latent representations for images and corresponding ground truth masks, followed by a lightweight model that learns a mapping function from the image to the mask in the latent space. Once trained, the model can generate estimated segmentation masks using the pre-trained vision foundation model to decode the predicted latent mask representation back into image space. The design of GMS leads to fewer trainable parameters in the model, reducing the risk of overfitting and enhancing its generalization capability. Our experimental analysis across five open-source datasets in different medical imaging domains demonstrates GMS outperforms existing discriminative and generative segmentation models. Furthermore, GMS is able to generalize well across datasets of the same imaging modality from different centers. Our experiments suggest GMS offers a scalable and effective solution for medical image segmentation.