CT image volumes are large and consume a significant portion of the memory available on a GPU. This limits the complexity of machine-learning model that can be applied. Octrees are an image representation which can improve memory utilization by compressing areas with similar image intensity. We've adapted an Octree-based machine-learning framework to perform segmentation of the myocardial blood pool.
First, we showed that a intensity threshold parameter can be used to control the extent to which the image is compressed. This can lead to significant reductions in memory utilization without impacting the quality of the image.
While increased levels of compression decreases image quality, it enables increased machine-learning model complexity. We evaluated the impact this trade-off has on the resulting segmentation and identified an improvement at intermediated levels of compression and model complexity.