  We present a method to segment MRI scans of the human brain into ischemic
stroke lesion and normal tissues. We propose a neural network architecture in
the form of a standard encoder-decoder where predictions are guided by a
spatial expansion embedding network. Our embedding network learns features that
can resolve detailed structures in the brain without the need for
high-resolution training images, which are often unavailable and expensive to
acquire. Alternatively, the encoder-decoder learns global structures by means
of striding and max pooling. Our embedding network complements the
encoder-decoder architecture by guiding the decoder with fine-grained details
lost to spatial downsampling during the encoder stage. Unlike previous works,
our decoder outputs at 2 times the input resolution, where a single pixel in
the input resolution is predicted by four neighboring subpixels in our output.
To obtain the output at the original scale, we propose a learnable downsampler
(as opposed to hand-crafted ones e.g. bilinear) that combines subpixel
predictions. Our approach improves the baseline architecture by approximately
11.7% and achieves the state of the art on the ATLAS public benchmark dataset
with a smaller memory footprint and faster runtime than the best competing
method. Our source code has been made available at:
https://github.com/alexklwong/subpixel-embedding-segmentation.
