  Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance in many different 2D medical image analysis tasks. In clinical
practice, however, a large part of the medical imaging data available is in 3D.
This has motivated the development of 3D CNNs for volumetric image segmentation
in order to benefit from more spatial context. Due to GPU memory restrictions
caused by moving to fully 3D, state-of-the-art methods depend on
subvolume/patch processing and the size of the input patch is usually small,
limiting the incorporation of larger context information for a better
performance. In this paper, we propose a novel Holistic Decomposition
Convolution (HDC), for an effective and efficient semantic segmentation of
volumetric images. HDC consists of a periodic down-shuffling operation followed
by a conventional 3D convolution. HDC has the advantage of significantly
reducing the size of the data for sub-sequential processing while using all the
information available in the input irrespective of the down-shuffling factors.
Results obtained from comprehensive experiments conducted on hip T1 MR images
and intervertebral disc T2 MR images demonstrate the efficacy of the present
approach.
