  Many renal cancers are incidentally found on non-contrast CT (NCCT) images.
On contrast-enhanced CT (CECT) images, most kidney tumors, especially renal
cancers, have different intensity values compared to normal tissues. However,
on NCCT images, some tumors called isodensity tumors, have similar intensity
values to the surrounding normal tissues, and can only be detected through a
change in organ shape. Several deep learning methods which segment kidney
tumors from CECT images have been proposed and showed promising results.
However, these methods fail to capture such changes in organ shape on NCCT
images. In this paper, we present a novel framework, which can explicitly
capture protruded regions in kidneys to enable a better segmentation of kidney
tumors. We created a synthetic mask dataset that simulates a protuberance, and
trained a segmentation network to separate the protruded regions from the
normal kidney regions. To achieve the segmentation of whole tumors, our
framework consists of three networks. The first network is a conventional
semantic segmentation network which extracts a kidney region mask and an
initial tumor region mask. The second network, which we name protuberance
detection network, identifies the protruded regions from the kidney region
mask. Given the initial tumor region mask and the protruded region mask, the
last network fuses them and predicts the final kidney tumor mask accurately.
The proposed method was evaluated on a publicly available KiTS19 dataset, which
contains 108 NCCT images, and showed that our method achieved a higher dice
score of 0.615 (+0.097) and sensitivity of 0.721 (+0.103) compared to 3D-UNet.
To the best of our knowledge, this is the first deep learning method that is
specifically designed for kidney tumor segmentation on NCCT images.
