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Quantification of Breast Cancer Cell Invasiveness Using a Three-dimensional 3D Model
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Mitosis Detection for Invasive Breast Cancer Grading in Histopathological Images.

Angshuman Paul, Dipti Prasad Mukherjee

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |July 29, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an automated method for detecting mitotic figures in breast cancer histopathology images, improving accuracy and reducing pathologist workload. The novel approach enhances cancer grading and personalized treatment planning.

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    Area of Science:

    • Computational pathology
    • Digital image analysis
    • Cancer diagnostics

    Background:

    • Accurate histopathological grading is crucial for cancer prognosis and treatment planning.
    • Mitosis counting in invasive breast cancer grading is labor-intensive and subject to observer variability.
    • Manual analysis of thousands of images per patient is tedious for pathologists.

    Purpose of the Study:

    • To develop a fast and accurate automated approach for mitosis detection in histopathological images.
    • To improve the precision of cell segmentation and classification for mitotic figures.
    • To reduce observer variability in cancer grading.

    Main Methods:

    • Utilized area morphological scale space for precise cell segmentation.
    • Employed a novel scale space construction maximizing relative-entropy for cell-background distinction.
    • Applied a random forest classifier to categorize segmented cells as mitotic or non-mitotic.

    Main Results:

    • Achieved precise cell segmentation through a novel scale space approach.
    • Demonstrated at least a 12% improvement in F1 score for mitosis detection.
    • Validated the method on over 450 histopathological images at 40× magnification.

    Conclusions:

    • The proposed automated mitosis detection method is fast, accurate, and reliable.
    • This approach can significantly aid pathologists in cancer grading, reducing workload and variability.
    • Enhanced mitosis detection contributes to more precise patient prognosis and individualized treatment strategies.