  Semantic segmentation for medical 3D image stacks enables accurate volumetric
reconstructions, computer-aided diagnostics and follow up treatment planning.
In this work, we present a novel variant of the Unet model called the NUMSnet
that transmits pixel neighborhood features across scans through nested layers
to achieve accurate multi-class semantic segmentations with minimal training
data. We analyze the semantic segmentation performance of the NUMSnet model in
comparison with several Unet model variants to segment 3-7 regions of interest
using only 10% of images for training per Lung-CT and Heart-CT volumetric image
stacks. The proposed NUMSnet model achieves up to 20% improvement in
segmentation recall with 4-9% improvement in Dice scores for Lung-CT stacks and
2.5-10% improvement in Dice scores for Heart-CT stacks when compared to the
Unet++ model. The NUMSnet model needs to be trained by ordered images around
the central scan of each volumetric stack. Propagation of image feature
information from the 6 nested layers of the Unet++ model are found to have
better computation and segmentation performances than propagation of all
up-sampling layers in a Unet++ model. The NUMSnet model achieves comparable
segmentation performances to existing works, while being trained on as low as
5\% of the training images. Also, transfer learning allows faster convergence
of the NUMSnet model for multi-class semantic segmentation from pathology in
Lung-CT images to cardiac segmentations in Heart-CT stacks. Thus, the proposed
model can standardize multi-class semantic segmentation on a variety of
volumetric image stacks with minimal training dataset. This can significantly
reduce the cost, time and inter-observer variabilities associated with
computer-aided detections and treatment.
