  Segmentation in 3D scans is playing an increasingly important role in current
clinical practice supporting diagnosis, tissue quantification, or treatment
planning. The current 3D approaches based on convolutional neural networks
usually suffer from at least three main issues caused predominantly by
implementation constraints - first, they require resizing the volume to the
lower-resolutional reference dimensions, second, the capacity of such
approaches is very limited due to memory restrictions, and third, all slices of
volumes have to be available at any given training or testing time. We address
these problems by a U-Net-like architecture consisting of bidirectional
convolutional LSTM and convolutional, pooling, upsampling and concatenation
layers enclosed into time-distributed wrappers. Our network can either process
the full volumes in a sequential manner, or segment slabs of slices on demand.
We demonstrate performance of our architecture on vertebrae and liver
segmentation tasks in 3D CT scans.
