scholarly journals A Coarse-to-fine Framework for Automated Kidney and Kidney Tumor Segmentation from Volumetric CT Images

2019 ◽  
Author(s):  
Huai Chen ◽  
Xiyi Wu ◽  
Yijie Huang ◽  
Lisheng Wang
Keyword(s):  
Ct Images ◽  
Tumor Segmentation ◽  
Kidney Tumor ◽  
Volumetric Ct ◽  
Coarse To Fine

An international challenge to use artificial intelligence to define the state-of-the-art in kidney and kidney tumor segmentation in CT imaging.

2020 ◽  
Vol 38 (6_suppl) ◽  
pp. 626-626
Author(s):  
Nicholas Heller ◽  
Sean McSweeney ◽  
Matthew Thomas Peterson ◽  
Sarah Peterson ◽  
Jack Rickman ◽  
...  
Keyword(s):  
Deep Learning ◽  
Treatment Decision ◽  
Semantic Segmentation ◽  
Ct Images ◽  
Renal Tumors ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Kidney Tumor ◽  
3D Segmentation ◽  
Kidney Tumors

626 Background: The 2019 Kidney and Kidney Tumor Segmentation challenge (KiTS19) was an international competition held in conjunction with the 2019 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) and sought to stimulate progress on this automatic segmentation frontier. Growing rates of kidney tumor incidence led to research into the use of artificial inteligence (AI) to radiographically differentiate and objectively characterize these tumors. Automated segmentation using AI objectively quantifies complexity and aggression of renal tumors to better differentiate and describe the tumors for improved treatment decision making. Methods: A training set of over 31,000 CT images from 210 patients with kidney tumors was publicly released with corresponding semantic segmentation masks. 106 teams from five continents used this data to develop automated deep learning systems to predict the true segmentation masks on a test set of an additional 13,500 CT images in 90 patients for which the corresponding ground truth segmentations were kept private. These predictions were scored and ranked according to their average Sørensen-Dice coefficient between kidney and tumor across the 90 test cases. Results: The winning team achieved a Dice of 0.974 for kidney and 0.851 for tumor, approaching the human inter-annotator performance on kidney (0.983) but falling short on tumor (0.923). This challenge has now entered an “open leaderboard” phase where it serves as a challenging benchmark in 3D semantic segmentation. Conclusions: Results of the KiTS19 challenge show deep learning methods are fully capable of reliable segmentation of kidneys and kidney tumors. The KiTS19 challenge attracted a high number of submissions and serves as an important and challenging benchmark in 3D segmentation. The publicly available data will further propel the use of automated 3D segmentation analysis. Fully segmented kidneys and tumors allow for automated calculation of all types of nephrometry, tumor textural variation and discovery of new predictive features important for personalized medicine and accurate prediction of patient relevant outcomes.

scholarly journals Crossbar-Net: A Novel Convolutional Neural Network for Kidney Tumor Segmentation in CT Images

2019 ◽  
Vol 28 (8) ◽  
pp. 4060-4074 ◽  
Author(s):  
Qian Yu ◽  
Yinghuan Shi ◽  
Jinquan Sun ◽  
Yang Gao ◽  
Jianbing Zhu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Ct Images ◽  
Tumor Segmentation ◽  
Kidney Tumor

An Ensemble of U-Net Models for Kidney Tumor Segmentation with CT images

2021 ◽  
pp. 1-1
Author(s):  
Jason Causey ◽  
Jonathan Stubblefield ◽  
Jake Qualls ◽  
Jennifer Fowler ◽  
Lingrui Cai ◽  
...  
Keyword(s):  
Ct Images ◽  
Tumor Segmentation ◽  
Kidney Tumor

Submission to the Kidney Tumor Segmentation Challenge 2019

2019 ◽  
Author(s):  
Pengxin Yu ◽  
Xing Cui ◽  
Xi Tian ◽  
Jiechao Ma ◽  
Rongguo Zhang
Keyword(s):  
Tumor Segmentation ◽  
Kidney Tumor
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