An Ensemble of 2.5D ResUnet Based Models for Segmentation for Kidney and
  Masses
Cancan Chen RongguoZhang
The automatic segmentation of kidney, kidney tumor and kidney cyst on
Computed Tomography (CT) scans is a challenging task due to the indistinct
lesion boundaries and fuzzy texture. Considering the large range and unbalanced
distribution of CT scans' thickness, 2.5D ResUnet are adopted to build an
efficient coarse-to-fine semantic segmentation framework in this work. A set of
489 CT scans are used for training and validation, and an independent
never-before-used CT scans for testing. Finally, we demonstrate the
effectiveness of our proposed method. The dice values on test set are 0.954,
0.792, 0.691, the surface dice values are 0.897, 0.591, 0.541 for kidney, tumor
and cyst, respectively. The average inference time of each CT scan is 20.65s
and the max GPU memory is 3525MB. The results suggest that a better trade-off
between model performance and efficiency.