Segmentation of kidney on CT images is critical to
computer-assisted surgical planning for kidney interventional
therapy. Segmenting kidney manually is impractical in clinical,
automatic segmentation is desirable. U-Net has been successful in
medical image segmentation and is a promising candidate for the
task. However, semantic gap still exists, especially when multiple
phase images or multiple center images are involved. In this paper,
we proposed an ULBNet to reduce the semantic gap and to
improve segmentation performance. The proposed architecture
includes new skip connections of local binary convolution (LBC).
We also proposed a novel strategy of fast retraining a model for a
new task without manually labelling required. We evaluated the
network for kidney segmentation on multiple phase CT images.
ULBNet resulted in an overall accuracy of 98.0% with comparison
to Resunet 97.5%. Specifically, on the plain phase CT images,
98.1% resulted from ULBNet and 97.6% from Resunet; on the
corticomedullay phase images, 97.8% from ULBNet and 97.2%
from Resunet; on the nephrographic phase images, 97.6% from
ULBNet and 97.4% from Resunet; on the excretory phase images,
98.1% from ULBNet and 97.4% from Resunet. The proposed
network architecture performs better than Resunet on
generalizing to multiple phase images.
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Funding Info:
The work is partially funded by A*STAR funding ACCL/19-GAP035-R20H.