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

2019 ◽  
Author(s):  
Yi Lv ◽  
Junchen Wang
Keyword(s):  
Loss Function ◽  
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

scholarly journals Kidney Tumor Segmentation and Classification on Abdominal CT Scans

2017 ◽  
Vol 164 (9) ◽  
pp. 1-5 ◽  
Author(s):  
Bansari Shah ◽  
Charmi Sawla ◽  
Shraddha Bhanushali ◽  
Poonam Bhogale
Keyword(s):  
Ct Scans ◽  
Tumor Segmentation ◽  
Kidney Tumor ◽  
Abdominal Ct

scholarly journals Kidney and Kidney Tumor Segmentation using a Logical Ensemble of U-nets with Volumetric Validation

2019 ◽  
Author(s):  
Jamie A. O'Reilly ◽  
Manas Sangworasil ◽  
Takenobu Matsuura
Keyword(s):  
Tumor Segmentation ◽  
Kidney Tumor

scholarly journals Comparison of Unet3D Models for Kidney Tumor Segmentation

2020 ◽  
Author(s):  
Fuat Turk ◽  
Murat Luy ◽  
Necaattin Barisci
Keyword(s):  
Image Segmentation ◽  
Surgical Treatment ◽  
Kidney Cancer ◽  
Diagnostic Methods ◽  
Disease Spread ◽  
Tumor Segmentation ◽  
Kidney Tumor ◽  
Industrialized Countries ◽  
Kidney Tumors ◽  
Cancer Types

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.

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

2019 ◽  
Author(s):  
Liyan Sun ◽  
Weihong Zeng ◽  
Xinghao Ding ◽  
Yue Huang
Keyword(s):  
Ct Scans ◽  
Tumor Segmentation ◽  
Kidney Tumor ◽  
Attention Network ◽  
Multi Scale

scholarly journals BRAIN TUMOR SEGMENTATION BASED ON U-NET WITH IMAGE DRIVEN LEVEL SET LOSS

2021 ◽  
Vol 59 (5) ◽  
Author(s):  
Truong Van Pham ◽  
Thao Thi Tran
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Loss Function ◽  
High Performance ◽  
Deep Neural Networks ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Dice Coefficient ◽  
Brain Tumor Segmentation ◽  
Mri Scans

This paper presents an approach for brain tumor segmentation based on deep neural networks. The paper proposes to utilize U-Net as an architecture of the approach to capture the fine and soars information from input images. Especially, to train the network, instead of using commonly used cross-entropy loss, dice loss or both, in this study, we propose to employ a new loss function including Level set loss and Dice loss function. The level set loss is inspired from Mumford-Shah functional for unsupervised task. Meanwhile, the Dice loss function measures the similarity between the predicted mask and desired mask. The proposed approach is then applied to segment brain tumor from MRI images as well as evaluated and compared with other approaches on a dataset of nearly 4000 brain MRI scans. Experiment results show that the proposed approach achieves high performance in terms of Dice coefficient and Intersection over Union (IoU) scores.

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.

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