RAU-Net: U-Net Model Based on Residual and Attention for Kidney and Kidney Tumor Segmentation

Worldwide, hundreds of thousands of people are diagnosed with kidney cancer and this disease is more common in developed and industrialized countries. Previously, kidney cancer was known as an elderly disease and was seen in people over a certain age; nowadays it is also seen in younger individuals and it is easier to diagnose thanks to new radiological diagnostic methods. A kidney tumor is a type of cancer that is extremely aggressive and needs surgical treatment rapidly. Today, approximately 30% of patients diagnosed with kidney cancer are unfortunately noticed at the stage of metastatic disease (spread to distant organs). The biggest factor that pushes us to this study is that kidney tumors progress unlike other cancer types with little or no symptoms. Therefore, conducting such studies is extremely important for early diagnosis. In this study, we compare the Unet3D models in order to help people who are dealing with difficulties in the diagnosis of kidney cancer. Unet, Unet+ResNet and Unet++ models were compared for image segmentation.

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Multi-Encoder U-Net for Automatic Kidney Tumor Segmentation

10.24926/548719.024 ◽

2019 ◽

Author(s):

Xueying Chen ◽

Chao Xu

Keyword(s):

Tumor Segmentation ◽

Kidney Tumor

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A Multi-scale Attention Network for Kidney Tumor Segmentation on CT Scans

10.24926/548719.055 ◽

2019 ◽

Author(s):

Liyan Sun ◽

Weihong Zeng ◽

Xinghao Ding ◽

Yue Huang

Keyword(s):

Ct Scans ◽

Tumor Segmentation ◽

Kidney Tumor ◽

Attention Network ◽

Multi Scale

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Kidney Tumor Segmentation Based on U-Net and V-Net with Double Loss Function Training

10.24926/548719.054 ◽

2019 ◽

Author(s):

Yi Lv ◽

Junchen Wang

Keyword(s):

Loss Function ◽

Tumor Segmentation ◽

Kidney Tumor

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

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.6_suppl.626 ◽

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.

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