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Generative Consistency for Semi-Supervised Cerebrovascular Segmentation From TOF-MRA.

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    This study introduces a generative consistency for semi-supervised (GCS) model to improve cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) using limited labeled data. The GCS model enhances feature mining and segmentation accuracy in medical imaging.

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    Area of Science:

    • Medical Imaging
    • Artificial Intelligence
    • Computer-Aided Diagnosis

    Background:

    • Cerebrovascular segmentation from Time-of-flight magnetic resonance angiography (TOF-MRA) is crucial for computer-aided diagnosis.
    • Deep learning models excel at feature extraction but require extensive labeled datasets, which are costly and time-consuming to acquire.
    • Existing methods face challenges in achieving high accuracy with limited labeled medical data.

    Purpose of the Study:

    • To propose a novel generative consistency for semi-supervised (GCS) model for enhanced cerebrovascular segmentation.
    • To leverage unlabeled data and data augmentation to improve model performance with limited labeled TOF-MRA datasets.
    • To introduce a new Transformer-based graph space model as a backbone for improved feature representation.

    Main Methods:

    • Developed a generative consistency for semi-supervised (GCS) model that uses generated data from labeled, unlabeled, and perturbed unlabeled data to constrain the segmentation model.
    • Implemented a consistency calculation on perturbed data to enhance feature mining capabilities.
    • Proposed a novel backbone model that transforms TOF-MRA data into graph space and utilizes Transformer for correlation establishment.

    Main Results:

    • The proposed GCS model demonstrated effectiveness in cerebrovascular segmentation on TOF-MRA representations.
    • Experiments comparing the GCS model with state-of-the-art semi-supervised methods, using the proposed Transformer-based model as a backbone, showed significant improvements.
    • The GCS model plays a vital role in improving the accuracy and efficiency of cerebrovascular segmentation.

    Conclusions:

    • The generative consistency for semi-supervised (GCS) approach is effective for cerebrovascular segmentation, particularly when labeled data is scarce.
    • The integration of a Transformer-based graph space model as a backbone further enhances the performance of semi-supervised segmentation.
    • This work provides a valuable contribution to computer-aided diagnosis in neurovascular imaging.