  Recent advances in deep learning, like 3D fully convolutional networks
(FCNs), have improved the state-of-the-art in dense semantic segmentation of
medical images. However, most network architectures require severely
downsampling or cropping the images to meet the memory limitations of today's
GPU cards while still considering enough context in the images for accurate
segmentation. In this work, we propose a novel approach that utilizes
auto-context to perform semantic segmentation at higher resolutions in a
multi-scale pyramid of stacked 3D FCNs. We train and validate our models on a
dataset of manually annotated abdominal organs and vessels from 377 clinical CT
images used in gastric surgery, and achieve promising results with close to 90%
Dice score on average. For additional evaluation, we perform separate testing
on datasets from different sources and achieve competitive results,
illustrating the robustness of the model and approach.
