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Fully Automated Spleen Localization And Segmentation Using Machine Learning And 3D Active Contours.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    This summary is machine-generated.

    Automated spleen segmentation in CT scans is challenging. This study presents a novel method using classification models and active contours, achieving an average Dice score of 0.873 for accurate spleen localization and segmentation.

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    Area of Science:

    • Medical Imaging
    • Radiology
    • Computer-Aided Diagnosis

    Background:

    • Automated spleen segmentation in CT volumes is complex.
    • Variations in spleen size, shape, position, and intensity similarity with adjacent organs pose challenges.

    Purpose of the Study:

    • To develop and evaluate an automated method for spleen localization and segmentation in abdominal CT volumes.
    • To address the difficulties posed by anatomical variability and image characteristics.

    Main Methods:

    • Utilized trained classification models for initial localization.
    • Employed active contours guided by anatomical information and adaptive features for precise segmentation.
    • Tested on abdominal CT volumes from trauma patients across different contrast phases.

    Main Results:

    • Achieved an average Dice score of 0.873, indicating high segmentation accuracy.
    • Demonstrated robustness across diverse patient anatomies and imaging conditions.

    Conclusions:

    • The proposed method effectively automates spleen segmentation in abdominal CT.
    • This technique shows significant potential for clinical applications in radiology and trauma assessment.