  Deep Convolutional Neural Networks (CNNs) i.e. Residual Networks (ResNets)
have been used successfully for many computer vision tasks, but are difficult
to scale to 3D volumetric medical data. Memory is increasingly often the
bottleneck when training 3D Convolutional Neural Networks (CNNs). Recently,
invertible neural networks have been applied to significantly reduce activation
memory footprint when training neural networks with backpropagation thanks to
the invertible functions that allow retrieving its input from its output
without storing intermediate activations in memory to perform the
backpropagation.
  Among many successful network architectures, 3D Unet has been established as
a standard architecture for volumetric medical segmentation. Thus, we choose 3D
Unet as a baseline for a non-invertible network and we then extend it with the
invertible residual network. In this paper, we proposed two versions of the
invertible Residual Network, namely Partially Invertible Residual Network
(Partially-InvRes) and Fully Invertible Residual Network (Fully-InvRes). In
Partially-InvRes, the invertible residual layer is defined by a technique
called additive coupling whereas in Fully-InvRes, both invertible upsampling
and downsampling operations are learned based on squeezing (known as pixel
shuffle). Furthermore, to avoid the overfitting problem because of less
training data, a variational auto-encoder (VAE) branch is added to reconstruct
the input volumetric data itself. Our results indicate that by using
partially/fully invertible networks as the central workhorse in volumetric
segmentation, we not only reduce memory overhead but also achieve compatible
segmentation performance compared against the non-invertible 3D Unet. We have
demonstrated the proposed networks on various volumetric datasets such as iSeg
2019 and BraTS 2020.
