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  4. Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs
 
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Guided reconstruction with conditioned diffusion models for unsupervised anomaly detection in brain MRIs

Citation Link: https://doi.org/10.15480/882.14502
Publikationstyp
Journal Article
Date Issued
2025-01-22
Sprache
English
Author(s)
Behrendt, Finn  
Medizintechnische und Intelligente Systeme E-1  
Bhattacharya, Debayan  
Medizintechnische und Intelligente Systeme E-1  
Mieling, Till Robin  
Medizintechnische und Intelligente Systeme E-1  
Maack, Lennart  
Medizintechnische und Intelligente Systeme E-1  
Krüger, Julia  
Opfer, Roland  
Schlaefer, Alexander  
Medizintechnische und Intelligente Systeme E-1  
TORE-DOI
10.15480/882.14502
TORE-URI
https://tore.tuhh.de/handle/11420/53592
Journal
Computers in biology and medicine  
Volume
186
Article Number
109660
Citation
Computers in Biology and Medicine 186: 109660 (2025-01-22)
Publisher DOI
10.1016/j.compbiomed.2025.109660
Scopus ID
2-s2.0-85215554392
The application of supervised models to clinical screening tasks is challenging due to the need for annotated data for each considered pathology. Unsupervised Anomaly Detection (UAD) is an alternative approach that aims to identify any anomaly as an outlier from a healthy training distribution. A prevalent strategy for UAD in brain MRI involves using generative models to learn the reconstruction of healthy brain anatomy for a given input image. As these models should fail to reconstruct unhealthy structures, the reconstruction errors indicate anomalies. However, a significant challenge is to balance the accurate reconstruction of healthy anatomy and the undesired replication of abnormal structures. While diffusion models have shown promising results with detailed and accurate reconstructions, they face challenges in preserving intensity characteristics, resulting in false positives. We propose conditioning the denoising process of diffusion models with additional information derived from a latent representation of the input image. We demonstrate that this conditioning allows for accurate and local adaptation to the general input intensity distribution while avoiding the replication of unhealthy structures. We compare the novel approach to different state-of-the-art methods and for different data sets. Our results show substantial improvements in the segmentation performance, with the Dice score improved by 11.9%, 20.0%, and 44.6%, for the BraTS, ATLAS and MSLUB data sets, respectively, while maintaining competitive performance on the WMH data set. Furthermore, our results indicate effective domain adaptation across different MRI acquisitions and simulated contrasts, an important attribute for general anomaly detection methods. The code for our work is available at https://github.com/FinnBehrendt/Conditioned-Diffusion-Models-UAD.
Subjects
Brain MRI | Diffusion models | Segmentation | Unsupervised anomaly detection
MLE@TUHH
DDC Class
006: Special computer methods
Funding(s)
Projekt DEAL  
Publication version
publishedVersion
Lizenz
https://creativecommons.org/licenses/by/4.0/
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