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High Resolution Medical Image Analysis with Spatial Partitioning

Le Hou
Niki J. Parmar
Noam Shazeer
Xiaodan Song
Youlong Cheng
High Resolution Medical Image Analysis with Spatial Partitioning (2019)
Google Scholar


Medical images such as 3D computerized tomography (CT) scans, have a typical resolution of 512×512×512 voxels, three orders of magnitude more pixel data than ImageNet images. It is impossible to train CNN models directly on such high resolution images, because feature maps of a single image do not fit in the memory of single GPU/TPU. Existing image analysis approaches alleviate this problem by dividing (e.g. taking 2D slices of 3D scans) or down-sampling input images, which leads to complicated implementation and sub-optimal performance due to information loss. In this paper, we implement spatial partitioning, which internally distributes input and output of convolution operations across GPUs/TPUs. Our implementation is based on the Mesh-TensorFlow framework and is transparent to end users. To the best of our knowledge, this is the first work on training networks on 512×512×512 resolution CT scans end-to-end, without significant computational overhead.

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