  We address the computational barrier of deploying advanced deep learning
segmentation models in clinical settings by studying the efficacy of network
compression through tensor decomposition. We propose a post-training Tucker
factorization that enables the decomposition of pre-existing models to reduce
computational requirements without impeding segmentation accuracy. We applied
Tucker decomposition to the convolutional kernels of the TotalSegmentator (TS)
model, an nnU-Net model trained on a comprehensive dataset for automatic
segmentation of 117 anatomical structures. Our approach reduced the
floating-point operations (FLOPs) and memory required during inference,
offering an adjustable trade-off between computational efficiency and
segmentation quality. This study utilized the publicly available TS dataset,
employing various downsampling factors to explore the relationship between
model size, inference speed, and segmentation performance. The application of
Tucker decomposition to the TS model substantially reduced the model parameters
and FLOPs across various compression rates, with limited loss in segmentation
accuracy. We removed up to 88% of the model's parameters with no significant
performance changes in the majority of classes after fine-tuning. Practical
benefits varied across different graphics processing unit (GPU) architectures,
with more distinct speed-ups on less powerful hardware. Post-hoc network
compression via Tucker decomposition presents a viable strategy for reducing
the computational demand of medical image segmentation models without
substantially sacrificing accuracy. This approach enables the broader adoption
of advanced deep learning technologies in clinical practice, offering a way to
navigate the constraints of hardware capabilities.
