Posts Tagged ‘2D U-Net’
Complete List of Challenge Submissions
Kidney tumor segmentation using a 2D U-Net followed by a statistical post-processing filter
Paolucci, Iwan
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 timeconsuming 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 http://github.com/ipa/kits2019.
A 2D U-Net for Automated Kidney and Renal Tumor Segmentation
Chen, Joseph
Jin, Benson
Kidney and renal tumor segmentation are critical aspects to the diagnosis process. However, segmentation is a time consuming and tedious task, especially for volume segmentation. To help with this issue, we test a simple two-dimensional U-Net are architecture for automating the segmentation process for both regions of interests. In doing so, we found that the vanilla U-Net was able to achieve a local tumor-kidney test dice of 0.91 and tumor-only dice of 0.25 and leaderboard scores of 0.85 and 0.22.