This paper presents the challenge report for the 2021 Kidney and Kidney Tumor
Segmentation Challenge (KiTS21) held in conjunction with the 2021 international
conference on Medical Image Computing and Computer Assisted Interventions
(MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a
variety of innovations in how the challenge was designed, in addition to a
larger dataset. A novel annotation method was used to collect three separate
annotations for each region of interest, and these annotations were performed
in a fully transparent setting using a web-based annotation tool. Further, the
KiTS21 test set was collected from an outside institution, challenging
participants to develop methods that generalize well to new populations.
Nonetheless, the top-performing teams achieved a significant improvement over
the state of the art set in 2019, and this performance is shown to inch ever
closer to human-level performance. An in-depth meta-analysis is presented
describing which methods were used and how they faired on the leaderboard, as
well as the characteristics of which cases generally saw good performance, and
which did not. Overall KiTS21 facilitated a significant advancement in the
state of the art in kidney tumor segmentation, and provides useful insights
that are applicable to the field of semantic segmentation as a whole.

本文介绍 2021 年肾脏和肾脏肿瘤分割挑战 (KiTS21) 的挑战报告，该挑战目的是针对医学图像计算和计算机辅助干预领域的肾肿瘤分割进行不断改进和提高，以提供有用的见解。

KiTS21 挑战赛：基于皮质 - 髓质期 CT 图像的肾脏、肾肿瘤和肾囊肿自动分割

The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors,  and renal cysts in corticomedullary-phase CT

The morphometry of a kidney tumor revealed by contrast-enhanced Computed
Tomography (CT) imaging is an important factor in clinical decision making
surrounding the lesion's diagnosis and treatment. Quantitative study of the
relationship between kidney tumor morphology and clinical outcomes is difficult
due to data scarcity and the laborious nature of manually quantifying imaging
predictors. Automatic semantic segmentation of kidneys and kidney tumors is a
promising tool towards automatically quantifying a wide array of morphometric
features, but no sizeable annotated dataset is currently available to train
models for this task. We present the KiTS19 challenge dataset: A collection of
multi-phase CT imaging, segmentation masks, and comprehensive clinical outcomes
for 300 patients who underwent nephrectomy for kidney tumors at our center
between 2010 and 2018. 210 (70%) of these patients were selected at random as
the training set for the 2019 MICCAI KiTS Kidney Tumor Segmentation Challenge
and have been released publicly. With the presence of clinical context and
surgical outcomes, this data can serve not only for benchmarking semantic
segmentation models, but also for developing and studying biomarkers which make
use of the imaging and semantic segmentation masks.

本研究提出了一个基于自动语义分割的肾脏和肾脏肿瘤形态学特征检测方法，并建立了覆盖肾性肿瘤磨损模式的数据集，通过这个数据集开展肾性肿瘤分割的任务以提高磨损模式分析效率。

KiTS19 挑战赛数据：300 例肾肿瘤病例的临床背景、CT 语义分割和手术结果

The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context,  CT Semantic Segmentations, and Surgical Outcomes

Due to the irregular motion, similar appearance and diverse shape, accurate
segmentation of kidney tumor in CT images is a difficult and challenging task.
To this end, we present a novel automatic segmentation method, termed as
Crossbar-Net, with the goal of accurate segmenting the kidney tumors. Firstly,
considering that the traditional learning-based segmentation methods normally
employ either whole images or squared patches as the training samples, we
innovatively sample the orthogonal non-squared patches (namely crossbar
patches), to fully cover the whole kidney tumors in either horizontal or
vertical directions. These sampled crossbar patches could not only represent
the detailed local information of kidney tumor as the traditional patches, but
also describe the global appearance from either horizontal or vertical
direction using contextual information. Secondly, with the obtained crossbar
patches, we trained a convolutional neural network with two sub-models (i.e.,
horizontal sub-model and vertical sub-model) in a cascaded manner, to integrate
the segmentation results from two directions (i.e., horizontal and vertical).
This cascaded training strategy could effectively guarantee the consistency
between sub-models, by feeding each other with the most difficult samples, for
a better segmentation. In the experiment, we evaluate our method on a real CT
kidney tumor dataset, collected from 94 different patients including 3,500
images. Compared with the state-of-the-art segmentation methods, the results
demonstrate the superior results of our method on dice ratio score, true
positive fraction, centroid distance and Hausdorff distance. Moreover, we have
extended our crossbar-net to a different task: cardiac segmentation, showing
the promising results for the better generalization.

本文提出了一种名为 “Crossbar-Net” 的新型自动分割方法，该方法通过 “十” 字路口状图案的非正方形补丁进行训练，并使用级联卷积神经网络进行水平和垂直方向的分割结果集成，该方法在真实的 CT 肾肿瘤数据集上的表现优于最先进的分割方法，并获得更好的一致性和泛化表现。