Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascular diseases. Different imaging modalities utilize distinct principles to visualize the cerebral vasculature, which leads to the limitations of expensive annotations and performance degradation while training and deploying deep learning models. In this paper, we propose an unsupervised domain adaptation framework CereTS to perform translation and segmentation of cross-modality unpaired cerebral angiography. Considering the commonality of vascular structures and stylistic textures as domain-invariant and domain-specific features, CereTS adopts a multi-level domain alignment pattern that includes an image-level cyclic geometric consistency constraint, a patch-level masked contrastive constraint and a feature-level semantic perception constraint to shrink domain discrepancy while preserving consistency of vascular structures. Conducted on a publicly available TOF-MRA dataset and a private CTA dataset, our experiment shows that CereTS outperforms current state-of-the-art methods by a large margin. Code is available at https://github.com/mileswyn/CereTS.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1109/JBHI.2024.3523103 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Unsupervised semantic label generation in agricultural fields.
Front Robot AI
February 2025
Center for Robotics, University of Bonn, Bonn, Germany.
Robust perception systems allow farm robots to recognize weeds and vegetation, enabling the selective application of fertilizers and herbicides to mitigate the environmental impact of traditional agricultural practices. Today's perception systems typically rely on deep learning to interpret sensor data for tasks such as distinguishing soil, crops, and weeds. These approaches usually require substantial amounts of manually labeled training data, which is often time-consuming and requires domain expertise.
View Article and Find Full Text PDFCross-Domain Diffusion with Progressive Alignment for Efficient Adaptive Retrieval.
IEEE Trans Image Process
March 2025
Unsupervised efficient domain adaptive retrieval aims to transfer knowledge from a labeled source domain to an unlabeled target domain, while maintaining low storage cost and high retrieval efficiency. However, existing methods typically fail to address potential noise in the target domain, and directly align high-level features across domains, thus resulting in suboptimal retrieval performance. To address these challenges, we propose a novel Cross-Domain Diffusion with Progressive Alignment method (COUPLE).
View Article and Find Full Text PDFDeep Radon Prior: A fully unsupervised framework for sparse-view CT reconstruction.
Comput Biol Med
March 2025
Institute of Nuclear and New Energy Technology, Tsinghua University, Beijing, China; Beijing Key Laboratory of Nuclear Detection Technology, Beijing, China. Electronic address:
Background: Sparse-view computed tomography (CT) substantially reduces radiation exposure but often introduces severe artifacts that compromise image fidelity. Recent advances in deep learning for solving inverse problems have shown considerable promise in enhancing CT reconstruction; however, most approaches heavily rely on high-quality training datasets and lack interpretability.
Method: To address these challenges, this paper introduces a novel, fully unsupervised deep learning framework that mitigates the dependency on extensive labeled data and improves the interpretability of the reconstruction process.
Structure Preserving Cycle-Gan for Unsupervised Medical Image Domain Adaptation.
Annu Int Conf IEEE Eng Med Biol Soc
July 2024
The presence of domain shift in medical imaging is a common issue, which can greatly impact the performance of segmentation models when dealing with unseen image domains. This work introduces the Structure Preserving Cycle-GAN (SP Cycle-GAN) for unsupervised domain adaptation (DA) of segmentation datasets, which promotes medical structure preservation during image translation through the enforcement of a segmentation loss term in the overall Cycle-GAN training process. We demonstrate the structure preserving capability of the SP Cycle-GAN both visually and through comparison of Dice score segmentation performance for the un-supervised domain adaptation models.
View Article and Find Full Text PDFExploring Schizophrenia Classification in fMRI Data: A Common Spatial Patterns(CSP) Approach for Enhanced Feature Extraction and Classification.
Annu Int Conf IEEE Eng Med Biol Soc
July 2024
In the exploration of dynamic changes in network connectivity within resting-state functional magnetic resonance imaging (rs-fMRI), the dominant focus has traditionally been on a holistic study of the entire brain. Various methodologies and analyses have been applied in prior research within this domain. This study takes a novel approach by delving into a comparative analysis of the similarities between electroencephalogram (EEG) signals with motor imagery tasks and rs-fMRI signal.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!