Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation笔记
2017-11-10 16:48
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Joint Sequence Learning and Cross-Modality Convolution for 3D Biomedical Segmentation笔记
传统的图像都是二维的自然图像,使用CNN做一个end-to-end的架构,做一个以图生图的CNN框架,得到图像的分割结果。但是在医学的MRI图像中,脑部的扫描结果是3D并且多模态的数据。人的大脑本来就是3唯,这个比较好理解,多模态指的是从四个不同的不同的方向进行扫描,得到大脑不同区域的2位平面图(有错误请指出)。传统的方法直接使用3唯的切片进行分割,之后再做一个concat,忽略了每个切片之间的关联,给予上述问题,作者提出了使用多模态的CNN架构,同时使用convLSTM来模拟不同channel之间的关系,得到了很好的performance。
传统的图像都是二维的自然图像,使用CNN做一个end-to-end的架构,做一个以图生图的CNN框架,得到图像的分割结果。但是在医学的MRI图像中,脑部的扫描结果是3D并且多模态的数据。人的大脑本来就是3唯,这个比较好理解,多模态指的是从四个不同的不同的方向进行扫描,得到大脑不同区域的2位平面图(有错误请指出)。传统的方法直接使用3唯的切片进行分割,之后再做一个concat,忽略了每个切片之间的关联,给予上述问题,作者提出了使用多模态的CNN架构,同时使用convLSTM来模拟不同channel之间的关系,得到了很好的performance。
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