Three-dimensional (3D) kidney parsing on computed tomography angiography (CTA) images is one of the most important tasks for surgery-based renal cancer treatment (i.e., laparoscopic partial nephrectomy (LPN) [1]). It targets segmenting 3D kidneys, renal tumors, arteries, and veins. Once successful, clinicians will benefit from the 3D visual model of renal structures for accurate preoperative planning [2]. Preoperatively, the renal arteries will help estimate the renal perfusion model [3], so that the clinicians will select the tumor-feeding arterial branches and locate the arterial clamping position easily [4]. The tumor and kidney models will visually show the lesion regions, thus helping the pre-plan of the tumor resection surface. Intraoperatively, the preoperative plan will be displayed on the screen together with laparoscopic videos to guide the LPN [5]. Renal vessels (veins, arteries) outside the hilum will show a clear arterial clamping region visually, thus the clinicians will select arterial clamping branches quickly. The 3D visual model will also guide the clinicians in making appropriate decisions. Therefore, the costs of treatment will be reduced, the quality of LPN will be improved, and the pain of patients will be relieved. However, the field of kidney parsing has so far been limited due to challenges in complex and multiple structures, and due to the lack of public, curated, and annotated groundtruth data. 1) The large variations of the structures from different patients make the annotation an extremely challenging process. For example, the difference of multiple tumor subtypes makes the large shape, location and volume variation, and the complex growth patterns and extremely thin structures of the renal vessels also bring large voxel-level variations. 2) As a multi-structure segmentation task, the contradiction between the inter-structure difference and the preference of different segmentation methods have also become a large bottleneck. For example, the kidneys are large and lentil- like, the tumors are approximately spherical and the vessels are curved and slender. Based on the great clinical significance and inherent challenges, we organize a new challenge named Kidney Parsing (KiPA) for Renal Cancer Treatment 2022 Challenge. The KiPA challenge is an important step in the development of reliable, valid, and reproducible methods which extract four kidney-related structures on CTA images to promote the surgery-based renal cancer treatment. We have collected 130 images, 70 for the training dataset, 30 for the closed testing dataset and 30 for the opened testing dataset. Dice, HD and AVD are adopted as evaluation metrics. This challenge will promote the renal cancer treatment, interactions between researchers and interdisciplinary communication. References [1] Shao, P., Qin, C., Yin, C., Meng, X., Ju, X., Li, J., Lv, Q., Zhang, W., Xu,Z., 2011. Laparoscopic partial nephrectomy with segmental renal artery clamping: technique and clinical outcomes. European urology 59, 849–855. [2] Porpiglia, F., Fiori, C., Checcucci, E., Amparore, D., Bertolo, R., 2018. Hyperaccuracy three-dimensional reconstruction is able to maximize the efficacy of selective clamping during robot-assisted partial nephrectomy for complex renal masses. European urology 74, 651–660. [3] Zhang, S., Yang, G., Tang, L., Lv, Q., Li, J., Xu, Y., Zhu, X., Li, P., Shao, P., Wang, Z., 2019. Application of a functional3-dimensional perfusion model in laparoscopic partial nephrectomy with precise segmental renal artery clamping. Urology 125, 98–103. [4] Shao, P., Tang, L., Li, P., Xu, Y., Qin, C., Cao, Q., Ju, X., Meng, X., Lv, Q., Li, J., et al., 2012. Precise segmental renal artery clamping under the guidance of dual-source computed tomography angiography during laparoscopic partial nephrectomy. European urology 62, 1001–1008 [5] Nicolau, S., Soler, L., Mutter, D., Marescaux, J., 2011. Augmented reality in laparoscopic surgical oncology. Surgical oncology 20, 189–201 [6] Yang, G., Gu, J., Chen, Y., Liu, W., Tang, L., Shu, H., Toumoulin, C., 2014. Automatic kidney segmentation in ct images based on multi-atlas image registration, in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE. pp. 5538–5541. [7] He, Y., Yang, G., Yang, J., Ge, R., Kong, Y., Zhu, X., Shao,P., Shu H., Dillenseger, J.L., Li, S., 2021. Meta grayscale adaptive network for 3D integrated renal structures segmentation. Medical image analysis 71, 102055. [8] He, Y., Yang, G., Yang, J., Chen, Y., Kong, Y., Wu, J., Tang, L., Zhu, X., Dillenseger, J.L., Shao, P., Zhang, S., Shu, H., Coatrieux, J.L., Li, S., 2020. Dense biased networks with deep priori anatomy and hard region adaptation: Semisupervised learning for fine renal artery segmentation. Medical Image Analysis 63, 101722.