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.

Accurate brain lesion delineation is important for planning neurosurgical treatment. Automatic brain lesion segmentation methods based on convolutional neural networks have demonstrated remarkable performance. However, neural network performance is constrained by the lack of large-scale well-annotated training datasets. In this manuscript, we propose a comprehensive framework to efficiently generate new samples for training a brain lesion segmentation model. We first train a self-supervised lesion generator based on the adversarial autoencoder to model lesion appearance and shape. Next, we utilize a novel image composition algorithm, Soft Poisson Blending, to seamlessly combine synthetic lesions and brain images to obtain training samples. Finally, to effectively train the brain lesion segmentation model with augmented images we introduce a new prototype consistence regularization to align real and synthetic features. Our framework is validated by extensive experiments on two public brain lesion segmentation datasets: ATLAS v2.0 and Shift MS. Our method outperforms existing brain image data augmentation schemes. For instance, our method improves the Dice from 50.36% to 60.23% compared to the UNet with conventional data augmentation techniques for the ATLAS v2.0 dataset.

Recently, automated medical image segmentation methods based on deep learning have achieved great success. However, they heavily rely on large annotated datasets, which are costly and time-consuming to acquire. Few-shot learning aims to overcome the need for annotated data by using a small labeled dataset, known as a support set, to guide predicting labels for new, unlabeled images, known as the query set. Inspired by this paradigm, we introduce MatchSeg, a novel framework that enhances medical image segmentation through strategic reference image matching. We leverage contrastive language-image pre-training (CLIP) to select highly relevant samples when defining the support set. Additionally, we design a Joint Attention module to strengthen the interaction between support and query features, facilitating a more effective knowledge transfer between support and query sets. We validated our method across four public datasets. Experimental results demonstrate superior segmentation performance and powerful domain generalization ability of MatchSeg against existing methods for domain-specific and cross-domain segmentation tasks. Our code is made available at https://github.com/keeplearning-again/MatchSeg

This paper introduces SKiT, a fast Key information Transformer for phase recognition of videos. Unlike previous methods that rely on complex models to capture long-term temporal information, SKiT accurately recognizes high-level stages of videos using an efficient key pooling operation. This operation records important key information by retaining the maximum value recorded from the beginning up to the current video frame, with a time complexity of ${\mathcal{O}}\left( 1 \right)$. Experimental results on Cholec80 and AutoLaparo surgical datasets demonstrate the ability of our model to recognize phases in an online manner. SKiT achieves higher performance than state-of-the-art methods with an accuracy of 92.5% and 82.9% on Cholec80 and AutoLaparo, respectively, while running the temporal model eight times faster (7ms v.s. 55ms) than LoViT, which uses ProbSparse to capture global information. We highlight that the inference time of SKiT is constant, and independent from the input length, making it a stable choice for keeping a record of important global information, that appears on long surgical videos, essential for phase recognition. To sum up, we propose an effective and efficient model for surgical phase recognition that leverages key global information. This has an intrinsic value when performing this task in an online manner on long surgical videos for stable real-time surgical recognition systems.