The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge

Heller, Nicholas; Isensee, Fabian; Maier‐Hein, Klaus H.; Hou, Xiaoshuai; Xie, Chao; Li, Fengyi; Yang, Nan; Mu, Guangrui; Lin, Zhiyong; Han, Miofei; Yao, Guang; Gao, Yaozong; Zhang, Yao; Wang, Yixin; Feng, Huanqing; Yang, Jiawei; Xiong, Guangwei; Tian, Jiang; Zhong, Cheng; Ma, Jun; Rickman, Jack; Deán, J. Luis; Stai, Bethany; Tejpaul, Resha; Oestreich, Makinna; Blake, Paul; Kaluzniak, Heather; Raza, Shaneabbas; Rosenberg, Joel; Moore, Keenan; Walczak, Edward; Rengel, Zachary; Edgerton, Zach; Vasdev, Ranveer; Peterson, Matthew; McSweeney, Sean; Peterson, Sarah; Kalapara, Arveen; Sathianathen, Niranjan; Papanikolopoulos, Nikolaos; Weight, Christopher J.

doi:10.1016/j.media.2020.101821

Cited by 371 publications

(203 citation statements)

References 35 publications

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“…It can be found that on the in‐domain testing set, the performance of lesion segmentation is not as good as the performance of lung segmentation (Table V), which means that tumor segmentation remains a challenging problem. This observation is in line with recent results in MICCAI tumor segmentation challenge, that is, liver tumor segmentation 38 and kidney tumor segmentation 39 the models almost fail to predict COVID‐19 infections on testing set, which highlights that the lesion appearances differ significantly among lung cancer, pleural effusion, and COVID‐19 infections in CT scans. …”

Section: Resultssupporting

confidence: 87%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

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214

171

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Purpose Accurate segmentation of lung and infection in COVID‐19 computed tomography (CT) scans plays an important role in the quantitative management of patients. Most of the existing studies are based on large and private annotated datasets that are impractical to obtain from a single institution, especially when radiologists are busy fighting the coronavirus disease. Furthermore, it is hard to compare current COVID‐19 CT segmentation methods as they are developed on different datasets, trained in different settings, and evaluated with different metrics. Methods To promote the development of data‐efficient deep learning methods, in this paper, we built three benchmarks for lung and infection segmentation based on 70 annotated COVID‐19 cases, which contain current active research areas, for example, few‐shot learning, domain generalization, and knowledge transfer. For a fair comparison among different segmentation methods, we also provide standard training, validation and testing splits, evaluation metrics and, the corresponding code. Results Based on the state‐of‐the‐art network, we provide more than 40 pretrained baseline models, which not only serve as out‐of‐the‐box segmentation tools but also save computational time for researchers who are interested in COVID‐19 lung and infection segmentation. We achieve average dice similarity coefficient (DSC) scores of 97.3%, 97.7%, and 67.3% and average normalized surface dice (NSD) scores of 90.6%, 91.4%, and 70.0% for left lung, right lung, and infection, respectively. Conclusions To the best of our knowledge, this work presents the first data‐efficient learning benchmark for medical image segmentation, and the largest number of pretrained models up to now. All these resources are publicly available, and our work lays the foundation for promoting the development of deep learning methods for efficient COVID‐19 CT segmentation with limited data.

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Section: Resultssupporting

confidence: 87%

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Wang

An³

et al. 2021

Medical Physics

Self Cite

214

171

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“…To the best of the authors knowledge, a dedicated challenge for renal image segmentation yet is not proposed. The only challenge on renal image data is the KiTS2019 challenge [150] on renal tumor segmentation, however, from CT images. For renal imaging such a challenge might be warranted to allow for method comparisons.…”

Section: Discussionmentioning

confidence: 99%

Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects

et al. 2021

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Magnetic resonance imaging has achieved an increasingly important role in the clinical workup of renal diseases such chronic kidney disease (CKD). A large panel of parameters have been proposed to diagnose CKD among them total kidney volume (TKV) which recently qualified as biomarker. Volume estimation in renal MRI is based on image segmentation of the kidney and/or its compartments. Beyond volume estimation renal segmentation supports also the quantification of other MR based parameters such as perfusion or filtration. The aim of the present article is to discuss the recent existing literature on renal image segmentation techniques and show today's limitations of the proposed techniques that might hinder clinical translation. We also provide pointers to open source software related to renal image segmentation.

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“…To this purpose we have used the KiTS19 Challenge kidney dataset. 15 In these images, the abdominal area of the patient, containing the kidneys is present. To simulate the material images we establish a threshold to diferentiate the bone and the rest of tissue, based on the Hounsfield units of the CT volumes in KiTS19.…”

Section: Antropomorphic Numerical Phantom Experimentsmentioning

confidence: 99%

A deep learning post-processing to enhance the maximum likelihood estimate of three material decomposition in photon counting spectral CT

Eguizabal

Persson

Grönberg

2021

Medical Imaging 2021: Physics of Medical Imaging

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The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge

Cited by 371 publications

References 35 publications

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Toward data‐efficient learning: A benchmark for COVID‐19 CT lung and infection segmentation

Kidney Segmentation in Renal Magnetic Resonance Imaging - Current Status and Prospects

A deep learning post-processing to enhance the maximum likelihood estimate of three material decomposition in photon counting spectral CT

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