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.

翻译：肾癌是全球最常见的恶性肿瘤之一，其临床征象包括血尿和腰背部不适，对患者造成极大困扰。基于手术的肾癌治疗方案（如腹腔镜肾部分切除术）依赖于在计算机断层扫描血管造影图像上对三维肾脏进行解析。为提高肾脏多结构分割的准确性，学界已提出多种自动分割技术。肾脏解剖结构的三维可视化模型将有助于临床医生在术前精准规划手术方案。然而，由于肾脏内部结构的多样性和边缘灰度值较低，清晰且准确地分离肾脏不同部位仍具挑战性。本文提出了一种用于多结构肾脏分割的通道扩展与轴向注意力捕捉网络（CANet）。本方案基于当前主流的nnU-Net架构设计：首先，通过扩展通道尺寸构建更庞大的网络结构，从而提供更广阔的视野以促进复杂结构信息的提取；其次，在解码器中集成轴向注意力捕捉模块，通过获取细节信息实现边缘精化。在KiPA2022数据集上的评估显示，本网络对肾脏、肿瘤、动脉和静脉的Dice评分分别达到95.8%、89.1%、87.5%和84.9%，最终在该挑战赛中位列第四名。