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

By N. Heller, N. Sathianathen, A. Kalapara, E. Walczak, K. Moore, H. Kaluzniak, J. Rosenberg, P. Blake, Z. Rengel, Makinna Oestreich and 10 more. arXiv.org, 2019

489 citations recorded by the source corpus.

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.

Subfields: Molecular Biology, Pulmonary and Respiratory Medicine, Surgery

View Paper: https://www.semanticscholar.org/paper/2bc472478247173b25aaf5e12f2604a74a8d2063

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In Molecular Biology

Citations in the corpus

Unified Focal loss: Generalising Dice and cross entropy-based losses to handle class imbalanced medical image segmentation (2021, methodology): “To evaluate our loss functions, we select the KiTS19 dataset (Heller et al., 2019), a highly class imbalanced, multi-class classification problem.”
Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, unknown): “[88] 3D CT, MRI LA dataset [89], KiTS dataset [90] pr Random noise, dropout”
CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection (2023, background): “The data set was acquired by different scanners and protocols at six different clinical sites, with a largely varying in-plane resolution from 0.55 to 1.0 mm and slice spacing from 0.45 to 6.0 mm. KiTS [23] includes 210 training cases and 90 testing cases with annotations provided by the University ”
AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation (2022, background): “The pre-trained models will be employed to infer on multiple target datasets, e.g., MSD-Spleen, MSD-Liver, MSD-Pancreas, and Kits.”
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge (2019, background): “A comprehensive description of the annotation protocol and quality assurance procedures can be found in Heller et al. (2019b).”
Medical Image Segmentation Review: The Success of U-Net (2022, background): “To further support this we find the top 9/10 participants of the Kidney Tumor Segmentation (KiTS) 2021 challenge hosted by the Medical Image Computing and Computer Assisted Intervention (MICCAI) 2021 society [93], [94] challenge utilized a 3D UNet and 1/10 utilized a 2D one 1.”
Automated Design of Deep Learning Methods for Biomedical Image Segmentation (2019, methodology): “Algorithmic optimization of the codependent design choices in this high dimensional space of hyperparameters is technically demanding and amplifies both the number of required training cases as well as compute resources by orders of magnitude [8].”
UNETR: Transformers for 3D Medical Image Segmentation (2021, background): “chitectures [22, 23, 21] have achieved state-of-the-art results in various medical semantic segmentation tasks [2, 38, 19].”
CaraNet: context axial reverse attention network for segmentation of small medical objects (2021, background): “They all achieved excellent performance on medical image segmentation challenges from different modalities, like BraTS [5,6,7], KiTS19 [8], and COVID19-20 [9,10].”
Medical Image Segmentation Using Deep Learning: A Survey (2020, unknown): “Thus there are many datasets on the brain and abdomen such as BRATS [3] [153] [154], ISLES [155], KITS [156], LITS [157], CHAOS [158], etc.”