  Direct automatic segmentation of objects from 3D medical imaging, such as
magnetic resonance (MR) imaging, is challenging as it often involves accurately
identifying a number of individual objects with complex geometries within a
large volume under investigation. To address these challenges, most deep
learning approaches typically enhance their learning capability by
substantially increasing the complexity or the number of trainable parameters
within their models. Consequently, these models generally require long
inference time on standard workstations operating clinical MR systems and are
restricted to high-performance computing hardware due to their large memory
requirement. Further, to fit 3D dataset through these large models using
limited computer memory, trade-off techniques such as patch-wise training are
often used which sacrifice the fine-scale geometric information from input
images which could be clinically significant for diagnostic purposes. To
address these challenges, we present a compact convolutional neural network
with a shallow memory footprint to efficiently reduce the number of model
parameters required for state-of-art performance. This is critical for
practical employment as most clinical environments only have low-end hardware
with limited computing power and memory. The proposed network can maintain data
integrity by directly processing large full-size 3D input volumes with no
patches required and significantly reduces the computational time required for
both training and inference. We also propose a novel loss function with extra
shape constraint to improve the accuracy for imbalanced classes in 3D MR
images.

Image and Video Processing;Computer Vision and Pattern Recognition