MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

By Junde Wu, Huihui Fang, Yu Zhang, Yehui Yang, Yanwu Xu. International Conference on Medical Imaging with Deep Learning, 2022

470 citations recorded by the source corpus.

Diffusion probabilistic model (DPM) recently becomes one of the hottest topic in computer vision. Its image generation application such as Imagen, Latent Diffusion Models and Stable Diffusion have shown impressive generation capabilities, which aroused extensive discussion in the community. Many recent studies also found it is useful in many other vision tasks, like image deblurring, super-resolution and anomaly detection. Inspired by the success of DPM, we propose the first DPM based model toward general medical image segmentation tasks, which we named MedSegDiff. In order to enhance the step-wise regional attention in DPM for the medical image segmentation, we propose dynamic conditional encoding, which establishes the state-adaptive conditions for each sampling step. We further propose Feature Frequency Parser (FF-Parser), to eliminate the negative effect of high-frequency noise component in this process. We verify MedSegDiff on three medical segmentation tasks with different image modalities, which are optic cup segmentation over fundus images, brain tumor segmentation over MRI images and thyroid nodule segmentation over ultrasound images. The experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods with considerable performance gap, indicating the generalization and effectiveness of the proposed model. Our code is released at https://github.com/WuJunde/MedSegDiff.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/c266aedf9660a70d15d02f53242c6fcebe7b4b8c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Huihui Fang
2. Junde Wu
3. Yu Zhang
4. Yanwu Xu

In Computer Vision and Pattern Recognition

1. Rao Fu
2. Huihui Fang
3. Junde Wu
4. Yu Zhang
5. Yanwu Xu

Citations in the corpus

• A Survey on Generative Diffusion Models (2022, unknown): “Segmentation & Detection AnoDDPM[219]/DiffAno[72] MedSegDiff[220]/DiffSeg[197]”
• Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation (2023, unknown): “In the tables, we compare MSA with the SAM model, as well as widely-used and well-recognized medical image segmentation methods, including nnUNet [19], TransUNet [3], UNetr [15], Swin-UNetr [14], EnsDiff [35], SegDiff [1], and MedSegDiff [36], all of which are based on either convolutional neural ne”
• MedSegDiff-V2: Diffusion based Medical Image Segmentation with Transformer (2023, methodology): “The remarkable performance of this model stems from its inherent stochastic sampling process(Wu et al. 2022c; Rahman et al. 2023).”