Technical report: Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter
This is an incremental improvement for clinicians and researchers in medical imaging, focusing on automating tumor delineation to study correlations with outcomes.
The paper tackled kidney tumor segmentation in 3D CT images to aid clinical decision-making, achieving results through a method combining a 2D U-Net with statistical post-processing, but no concrete numbers are provided.
Each year, there are about 400'000 new cases of kidney cancer worldwide causing around 175'000 deaths. For clinical decision making it is important to understand the morphometry of the tumor, which involves the time-consuming task of delineating tumor and kidney in 3D CT images. Automatic segmentation could be an important tool for clinicians and researchers to also study the correlations between tumor morphometry and clinical outcomes. We present a segmentation method which combines the popular U-Net convolutional neural network architecture with post-processing based on statistical constraints of the available training data. The full implementation, based on PyTorch, and the trained weights can be found on GitHub.