MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model

Junde Wu, RAO FU, Huihui Fang, Yu Zhang, Yehui Yang, Haoyi Xiong, Huiying Liu, Yanwu Xu
Medical Imaging with Deep Learning, PMLR 227:1623-1639, 2024.

Abstract

Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in computer vision. Its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, have demonstrated impressive generation capabilities, which have sparked extensive discussions in the community. Furthermore, many recent studies have found DPM to be useful in a variety of other vision tasks, including image deblurring, super-resolution, and anomaly detection. Inspired by the success of DPM, we propose MedSegDiff, the first DPM-based model for general medical image segmentation tasks. To enhance the step-wise regional attention in DPM for medical image segmentation, we propose Dynamic Conditional Encoding, which establishes state-adaptive conditions for each sampling step. Additionally, we propose the Feature Frequency Parser (FF-Parser) to eliminate the negative effect of high-frequency noise components in this process. We verify the effectiveness of MedSegDiff on three medical segmentation tasks with different image modalities, including optic cup segmentation over fundus images, brain tumor segmentation over MRI images, and thyroid nodule segmentation over ultrasound images. Our experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods by a considerable performance gap, demonstrating the generalization and effectiveness of the proposed model.

Cite this Paper

BibTeX
@InProceedings{pmlr-v227-wu24a, title = {MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model}, author = {Wu, Junde and FU, RAO and Fang, Huihui and Zhang, Yu and Yang, Yehui and Xiong, Haoyi and Liu, Huiying and Xu, Yanwu}, booktitle = {Medical Imaging with Deep Learning}, pages = {1623--1639}, year = {2024}, editor = {Oguz, Ipek and Noble, Jack and Li, Xiaoxiao and Styner, Martin and Baumgartner, Christian and Rusu, Mirabela and Heinmann, Tobias and Kontos, Despina and Landman, Bennett and Dawant, Benoit}, volume = {227}, series = {Proceedings of Machine Learning Research}, month = {10--12 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v227/wu24a/wu24a.pdf}, url = {https://proceedings.mlr.press/v227/wu24a.html}, abstract = {Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in computer vision. Its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, have demonstrated impressive generation capabilities, which have sparked extensive discussions in the community. Furthermore, many recent studies have found DPM to be useful in a variety of other vision tasks, including image deblurring, super-resolution, and anomaly detection. Inspired by the success of DPM, we propose MedSegDiff, the first DPM-based model for general medical image segmentation tasks. To enhance the step-wise regional attention in DPM for medical image segmentation, we propose Dynamic Conditional Encoding, which establishes state-adaptive conditions for each sampling step. Additionally, we propose the Feature Frequency Parser (FF-Parser) to eliminate the negative effect of high-frequency noise components in this process. We verify the effectiveness of MedSegDiff on three medical segmentation tasks with different image modalities, including optic cup segmentation over fundus images, brain tumor segmentation over MRI images, and thyroid nodule segmentation over ultrasound images. Our experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods by a considerable performance gap, demonstrating the generalization and effectiveness of the proposed model.} }
Endnote
%0 Conference Paper %T MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model %A Junde Wu %A RAO FU %A Huihui Fang %A Yu Zhang %A Yehui Yang %A Haoyi Xiong %A Huiying Liu %A Yanwu Xu %B Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2024 %E Ipek Oguz %E Jack Noble %E Xiaoxiao Li %E Martin Styner %E Christian Baumgartner %E Mirabela Rusu %E Tobias Heinmann %E Despina Kontos %E Bennett Landman %E Benoit Dawant %F pmlr-v227-wu24a %I PMLR %P 1623--1639 %U https://proceedings.mlr.press/v227/wu24a.html %V 227 %X Diffusion Probabilistic Model (DPM) has recently become one of the hottest topics in computer vision. Its image generation applications, such as Imagen, Latent Diffusion Models, and Stable Diffusion, have demonstrated impressive generation capabilities, which have sparked extensive discussions in the community. Furthermore, many recent studies have found DPM to be useful in a variety of other vision tasks, including image deblurring, super-resolution, and anomaly detection. Inspired by the success of DPM, we propose MedSegDiff, the first DPM-based model for general medical image segmentation tasks. To enhance the step-wise regional attention in DPM for medical image segmentation, we propose Dynamic Conditional Encoding, which establishes state-adaptive conditions for each sampling step. Additionally, we propose the Feature Frequency Parser (FF-Parser) to eliminate the negative effect of high-frequency noise components in this process. We verify the effectiveness of MedSegDiff on three medical segmentation tasks with different image modalities, including optic cup segmentation over fundus images, brain tumor segmentation over MRI images, and thyroid nodule segmentation over ultrasound images. Our experimental results show that MedSegDiff outperforms state-of-the-art (SOTA) methods by a considerable performance gap, demonstrating the generalization and effectiveness of the proposed model.
APA
Wu, J., FU, R., Fang, H., Zhang, Y., Yang, Y., Xiong, H., Liu, H. & Xu, Y.. (2024). MedSegDiff: Medical Image Segmentation with Diffusion Probabilistic Model. Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 227:1623-1639 Available from https://proceedings.mlr.press/v227/wu24a.html.

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