  There has been a debate in 3D medical image segmentation on whether to use 2D
or 3D networks, where both pipelines have advantages and disadvantages. 2D
methods enjoy a low inference time and greater transfer-ability while 3D
methods are superior in performance for hard targets requiring contextual
information. This paper investigates efficient 3D segmentation from another
perspective, which uses 2D networks to mimic 3D segmentation. To compensate the
lack of contextual information in 2D manner, we propose to thicken the 2D
network inputs by feeding multiple slices as multiple channels into 2D networks
and thus 3D contextual information is incorporated. We also put forward to use
early-stage multiplexing and slice sensitive attention to solve the confusion
problem of information loss which occurs when 2D networks face thickened
inputs. With this design, we achieve a higher performance while maintaining a
lower inference latency on a few abdominal organs from CT scans, in particular
when the organ has a peculiar 3D shape and thus strongly requires contextual
information, demonstrating our method's effectiveness and ability in capturing
3D information. We also point out that "thickened" 2D inputs pave a new method
of 3D segmentation, and look forward to more efforts in this direction.
Experiments on segmenting a few abdominal targets in particular blood vessels
which require strong 3D contexts demonstrate the advantages of our approach.

Computer Vision and Pattern Recognition