论文笔记：A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
大致框架： 基本流程： 预处理：通过N4ITK以及基于图像模式的方法实现图像强度归一化。 利用全卷积神经网络（FCNNs）对图像以像素单位分配不同标签。 利用条件随机场所构建的递归神经网络（CRF-RNN）对FCNNs输出结果进行优化。 利用多数投票策略（Majority voting strategy）对不同标签标记结果进行分割，实现病变脑组织与正常脑组织的划分。 后续处理：去噪、去除异常区域、
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相关文章
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