دانلود مقاله انگلیسی رایگان:تراکتوگرافی و یادگیری ماشین: وضعیت فعلی و چالش های باز - 2019
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  • Tractography and machine learning: Current state and open challenges Tractography and machine learning: Current state and open challenges
    Tractography and machine learning: Current state and open challenges

    سال انتشار:

    2019


    عنوان انگلیسی مقاله:

    Tractography and machine learning: Current state and open challenges


    ترجمه فارسی عنوان مقاله:

    تراکتوگرافی و یادگیری ماشین: وضعیت فعلی و چالش های باز


    منبع:

    Sciencedirect - Elsevier - Magnetic Resonance Imaging,Corrected proof,doi:10:1016/j:mri:2019:04:013


    نویسنده:

    Philippe Poulina,⁎, Daniel Jörgensb, Pierre-Marc Jodoina,1, Maxime Descoteauxa,1


    چکیده انگلیسی:

    Supervised machine learning (ML) algorithms have recently been proposed as an alternative to traditional tractography methods in order to address some of their weaknesses. They can be path-based and local-modelfree, and easily incorporate anatomical priors to make contextual and non-local decisions that should help the tracking process. ML-based techniques have thus shown promising reconstructions of larger spatial extent of existing white matter bundles, promising reconstructions of less false positives, and promising robustness to known position and shape biases of current tractography techniques. But as of today, none of these ML-based methods have shown conclusive performances or have been adopted as a de facto solution to tractography. One reason for this might be the lack of well-defined and extensive frameworks to train, evaluate, and compare these methods. In this paper, we describe several datasets and evaluation tools that contain useful features for ML algorithms, along with the various methods proposed in the recent years. We then discuss the strategies that are used to evaluate and compare those methods, as well as their shortcomings. Finally, we describe the particular needs of ML tractography methods and discuss tangible solutions for future works.
    Keywords: Diffusion MRI | Tractography | Machine learning | Benchmark


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 993 کیلوبایت

    قیمت: رایگان


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