Augmenting auto-context with global features – MICCAI MLMI

Hey! Our work got accepted into the 2013 MICCAI MLMI workshop and I got to go present our paper in Nagoya, Japan!

The poster we presented is here:
augAuto_MLMI2013

Our paper examined how to segment the spinal cord. We took a machine learning approach, specifically we used auto-context, to do a voxel-wise labeling (i.e. label the voxel as spinal cord or background) and used this labeling as our segmentation.

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Our main contribution is that in each stage of auto-context, we considered the connected components with the same label as a single candidate shape. Then, given this shape, we can compute features from the shape. These features we then use as input to the classifier in the next stage of auto-context.

We compared this approach to a more analytical approach we tried earlier and found that our new machine learning based approach gave superior results.

Our paper entitled, Augmenting auto-context with global geometric features for spinal cord segmentation, can be read here.

I did a video talk on our approaches to do spinal cord segmentation which I linked to in this post here.

If for some reason you would like the hi-res PDF version of the poster, feel free to email me or leave a comment below and I’ll post it 🙂