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在頭部電腦斷層影像中描繪出偏移的中線

TRACING THE DEFORMED MIDLINE ON BRAIN CT

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[[abstract]]臨床醫師在評估腦部病變或外傷的病人影像時,中線偏移是判斷腦部受壓迫程度最重要的量化指標。依據顱內不同組織的生物力學特性,作者提出了單張頭部電腦斷層影像裡偏移中線的模型。本模型分為三部份﹕上下的直線代表堅韌的大腦鐮,分隔大腦的兩個半球;中間的部份則使用二次貝茲曲線來代表柔軟的腦實質的偏移。藉由計算此中線模型兩側像素的亮度差平方和,我們可以找到最為”對稱” 的偏移中線。利用基因演算法可以加速曲線參數最佳化的過程。我們利用本系統來分析某區域醫院一年內神經外科加護病房的81名病患的影像,並與人工判讀的中線偏移量做比較,結果令人滿意。

[[abstract]]Midline shift (MLS) is the most important quantitative feature clinicians use to evaluate the severity of brain compression by various pathologies. We proposed a model of the deformed midline according to the biomechanical properties of different types of intracranial tissue. The model comprised three segments. The upper and lower straight segments represented parts of the tough meninges separating two hemispheres, and the central curved segment, formed by a quadratic Bezier curve, represented the intervening soft brain tissue. For each point of the model, the intensity difference was calculated over 48 adjacent point pairs at each side. The deformed midline was considered ideal as summed square of the difference across all midline points approaches global minimum, simulating maximal bilateral symmetry. Genetic algorithm was applied to optimize the values of the three control points of the Bezier curve. Our system was tested on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines itself as well as the amount of midline shift were evaluated by human experts, with satisfactory results.

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zh-tw[[iso]]en_US

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博碩士論文

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