  Renal cancer is one of the most prevalent cancers worldwide. Clinical signs
of kidney cancer include hematuria and low back discomfort, which are quite
distressing to the patient. Some surgery-based renal cancer treatments like
laparoscopic partial nephrectomy relys on the 3D kidney parsing on computed
tomography angiography (CTA) images. Many automatic segmentation techniques
have been put forward to make multi-structure segmentation of the kidneys more
accurate. The 3D visual model of kidney anatomy will help clinicians plan
operations accurately before surgery. However, due to the diversity of the
internal structure of the kidney and the low grey level of the edge. It is
still challenging to separate the different parts of the kidney in a clear and
accurate way. In this paper, we propose a channel extending and axial attention
catching Network(CANet) for multi-structure kidney segmentation. Our solution
is founded based on the thriving nn-UNet architecture. Firstly, by extending
the channel size, we propose a larger network, which can provide a broader
perspective, facilitating the extraction of complex structural information.
Secondly, we include an axial attention catching(AAC) module in the decoder,
which can obtain detailed information for refining the edges. We evaluate our
CANet on the KiPA2022 dataset, achieving the dice scores of 95.8%, 89.1%, 87.5%
and 84.9% for kidney, tumor, artery and vein, respectively, which helps us get
fourth place in the challenge.
