Uncertainty-generation-based diffusion probability model for brain tumor segmentation

Jiacheng Qin; Hao Zhang; YeJing Yuan; Kangjian He; Dan Xu

doi:10.1117/12.3029607

13 May 2024 Uncertainty-generation-based diffusion probability model for brain tumor segmentation

Jiacheng Qin, Hao Zhang, YeJing Yuan, Kangjian He, Dan Xu

Proceedings Volume 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024); 131580E (2024) https://doi.org/10.1117/12.3029607
Event: Seventh International Conference on Computer Graphics and Virtuality (ICCGV24), 2024, Hangzhou, China

Abstract

Brain tumor is one of the most dangerous diseases. Automated brain tumor segmentation technology is particularly important in the diagnosis and treatment of brain tumors. Traditional brain tumor segmentation methods mostly rely on UNet or associate variants, and the segmentation performance is highly dependent on the feature extraction quality. Recently, diffusion probabilistic model (DPM) has received a lot of attention and achieved remarkable success in medical image segmentation. However, the existing DPM-based brain tumor segmentation method did not utilize the advantages of complementary information between multimodal MRI. Additionally, they all constrained the generation of DPM using the original images. In this work, we propose a DPM-based brain tumor segmentation method, which consists of DPM, uncertainty generation module and collaborative Module. The collaborative module takes the input MRI from multimodal information and dynamically provide conditional constraints for DPM. This allows DPM to obtain more detailed brain tumor features. Considering that Previous works mainly ignore the influence of DPM's uncertainty on the results, we proposed an uncertainty generation module. It calculates the uncertainty of each step of the DPM and assigns corresponding uncertainty weights. The results of each step are fused according to inferred uncertainty weights to get the final segmentations. The proposed method obtained 89.32% and 87.82% dice scores on the BraTS2020 and BraTS2021 datasets, respectively, which verified the effectiveness of the proposed method.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jiacheng Qin, Hao Zhang, YeJing Yuan, Kangjian He, and Dan Xu "Uncertainty-generation-based diffusion probability model for brain tumor segmentation", Proc. SPIE 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024), 131580E (13 May 2024); https://doi.org/10.1117/12.3029607

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Tumors

Brain

Image segmentation

Diffusion

Feature extraction

Neuroimaging

Magnetic resonance imaging

Show All Keywords

Keywords/Phrases

Search In:

Publication Years