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MTCSNet: One-Stage Learning and Two-Point Labeling are Sufficient for Cell Segmentation.

Binyu Zhang, Zhu Meng, Hongyuan Li

    IEEE Transactions on Medical Imaging
    |May 23, 2024
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    This study introduces a weakly-supervised cell segmentation method (MTCSNet) using only two points per cell for annotation. MTCSNet significantly reduces annotation effort while achieving state-of-the-art performance in medical image analysis.

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    Area of Science:

    • Medical Image Analysis
    • Computational Biology
    • Artificial Intelligence

    Background:

    • Deep convolutional neural networks are crucial for medical image analysis tasks like cell segmentation.
    • Current methods often require extensive manual annotation, hindering efficiency.
    • Weakly supervised learning offers a solution but faces a performance gap compared to fully supervised methods.

    Purpose of the Study:

    • To develop a weakly-supervised cell segmentation method for multi-modal medical images.
    • To reduce the annotation burden for cell segmentation tasks.
    • To bridge the performance gap between weakly and fully supervised learning.

    Main Methods:

    • Proposed the Multi-Task Cell Segmentation Network (MTCSNet) for single-stage training.
    • Utilized only two annotated points (centroid and boundary) per cell for supervision.
    • Incorporated five auxiliary tasks: two pixel-level classifications, pixel-level regression, local temperature scaling, and instance-level distance regression.

    Main Results:

    • MTCSNet demonstrated superior performance compared to existing weakly-supervised cell segmentation methods.
    • Achieved state-of-the-art results on public multi-modal medical image datasets.
    • Validated the sufficiency of a two-point labeling approach for effective cell segmentation.

    Conclusions:

    • The proposed MTCSNet effectively performs cell segmentation with minimal annotation.
    • Single-stage learning with two-point labeling is a viable alternative to fine contour delineation.
    • This approach significantly reduces annotation workload in medical image analysis.