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Related Experiment Video

Updated: Jun 6, 2026

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators
09:51

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Published on: March 21, 2018

3D Segment Anything Model With Visual Mamba for Diagnosing Placenta Accreta Spectrum.

Yuliang Zhang, Fang He, Lulu Peng

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |June 4, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Placenta Accreta Spectrum (PAS) diagnosis is improved with 3DSAMba, a new framework using MRI segmentation. This AI tool enhances lesion identification, aiding early detection of this dangerous obstetric condition.

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    Published on: February 28, 2021

    Area of Science:

    • Obstetrics and Gynecology
    • Medical Imaging
    • Artificial Intelligence in Medicine

    Background:

    • Placenta Accreta Spectrum (PAS) is a rare but dangerous obstetric condition requiring early diagnosis.
    • Current PAS diagnosis relies on expert analysis of cesarean history and MRI, which is challenging in resource-limited hospitals.
    • Accurate PAS diagnosis is crucial for maternal health outcomes.

    Purpose of the Study:

    • To develop an automated system for accurate Placenta Accreta Spectrum diagnosis using MRI.
    • To create the first MRI-based PAS dataset with segmentation and classification annotations.
    • To enhance lesion segmentation in uterine MRI for improved PAS detection.

    Main Methods:

    • Proposed 3DSAMba, a novel feature learning framework for effective lesion segmentation in MRI.
    • Developed a 3D Segment Anything Model (SAM) with medical domain adaptation via an adapter mechanism.
    • Introduced Multi-Level Aggregation Mamba (MLAM) and Fusion State Space Model (FSSM) for advanced feature aggregation and fusion.

    Main Results:

    • The 3DSAMba framework significantly improved diagnostic performance for Placenta Accreta Spectrum.
    • Lesion areas were effectively isolated using segmentation masks applied to MRI images.
    • The developed dataset and source code are publicly available to advance PAS research.

    Conclusions:

    • The 3DSAMba framework offers a promising approach for automated and accurate PAS diagnosis.
    • AI-powered lesion segmentation in MRI can overcome resource limitations in diagnosing PAS.
    • This work facilitates further research and clinical application in obstetric imaging analysis.