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Related Concept Videos

In Vitro Fertilization01:24

In Vitro Fertilization

493
In vitro fertilization (IVF) is a form of assisted reproductive technology where an egg is fertilized with sperm in a controlled laboratory environment before transferring the resulting embryo into the uterus. This process is designed to help individuals and couples experiencing difficulties conceiving.
The IVF process begins with ovarian stimulation, during which reproductive endocrinologists prescribe hormonal medications to stimulate the ovaries to produce multiple eggs instead of the single...
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Ultrasonography01:17

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Ultrasonography is an imaging technique that uses high-frequency sound waves to visualize the body's internal structures. It is a non-invasive and safe procedure that does not involve the use of ionizing radiation, making it widely used in various medical fields. Ultrasonography is used to study heart function, blood flow in the neck or extremities, certain conditions such as gallbladder disease, and fetal growth and development.
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Unsupervised Deep Learning based Longitudinal Follicular Growth Tracking during IVF Cycle using 3D Transvaginal

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    Summary
    This summary is machine-generated.

    This study introduces an automated deep learning framework for tracking follicular growth during in-vitro fertilization (IVF) cycles. The novel system accurately registers and tracks individual follicles, improving assisted reproduction outcomes.

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    Area of Science:

    • Reproductive Medicine
    • Medical Imaging Analysis
    • Artificial Intelligence in Healthcare

    Background:

    • Longitudinal follicle tracking is crucial for assisted reproduction but is typically manual and error-prone.
    • Current methods rely on operator-dependent analysis, limiting accuracy and efficiency in in-vitro fertilization (IVF) cycles.
    • Automated follicular growth tracking is highly demanded to enhance IVF effectiveness and patient counseling.

    Purpose of the Study:

    • To develop and validate a two-stage deep learning framework for automated follicular registration and tracking.
    • To address the clinical need for accurate, operator-independent monitoring of follicular development during IVF.
    • To improve the precision of follicle count and growth estimation for personalized ovarian stimulation treatment.

    Main Methods:

    • A two-stage framework utilizing an unsupervised deep learning network (SFR-Net) for follicle registration and subsequent individual follicle tracking.
    • SFR-Net incorporates 3D U-Net and Multi-Scale Residual Blocks for registering follicles of varying sizes from 3D Transvaginal Ultrasound (3D TVUS) volumes.
    • Data comprised 96 volume pairs from 26 subjects, acquired every 2-3 days throughout the IVF cycle.

    Main Results:

    • Achieved an average DICE score of 85.84% for the follicle registration task on the test dataset.
    • Successfully demonstrated the ability to track individual follicles measuring above 4 mm.
    • This represents a novel approach to automated longitudinal tracking of follicular growth in assisted reproduction.

    Conclusions:

    • The proposed automated framework significantly enhances the accuracy and reliability of follicular growth tracking in IVF.
    • This technology has the potential to improve patient counseling and personalize ovarian stimulation protocols.
    • Automated tracking of follicular development is vital for optimizing assisted reproductive techniques and improving IVF success rates.