Posts Tagged ‘Two Stage Process’
Complete List of Challenge Submissions
Two stages kidney and tumor segmentation(kits2019)
Yiwen, Zhang
In order to accurately segment kidney tumors, this paper uses a twostage segmentation/ Stage one performs a coarse step positioning of the kidney. Stage two performs an accurate segmentation of the kidneys and tumors.
Two-Stage Method for Kidney and Tumor Segmentation Based On Cascade Res-VNet
Hao, Xiaoyu
In this work, We proposed a two-stage method for segmentation of kidneys and kidney tumors in CT images based on cascade Res-VNet. In the first stage, we consider the kidney and tumor as a whole region and use a cascade Res-VNet to do the segmentation. In the second stage, we firstly extract patches of ROIs based on the results of the first stage and use some tricks to improve precision. Then, we use another cascade Res-VNet to segment the tumor. Ouput from each stage are combined together as the final results.
KiTS19 Submission
Kondo, Satoshi
This report describes our method submitted to 2019 Kidney Tumor Segmentation (KiTS19) Challenge. Our method employs two step approach. In the first step, an input volume is divided into two-dimensional images in three orthogonal planes and the two-dimensional images are fed into encoder-decoder networks to segment kidney and tumor regions. In the second step, the segmentation results for three orthogonal planes are fed into convolutional neural networks to obtain final segmentation results. Although our method is based on twodimensional segmentation, but three-dimensional like processing can be performed by combining segmentation results in three orthogonal planes.
Using Two-stage Network to Segment Kidneys and Kidney Tumors
Chen, Pan
Xu, Chenghai
He, Jie
Sun, Chengwei
Ma, Yingying
Sun, Fenglong
There are many new cases of kidney cancer each year, and surgery is the most common treatment. To assist doctors in surgical planning, an accurate and automatic kidney and tumor segmentation method is helpful in the clinical practice. In this paper, we propose a deep learning framework for the segmentation of kidneys and tumors in abdominal CT images. The key idea is using a two-stage strategy. First, for each case, we use a 3d U-shape convolution network to get the localization of each kidney. Then using next 3d U-shape convolution network we obtain the precise segmentation results of each kidney. Finally, merge the results to obtain the complete segmentation. Also, we try some tricks to improve the performance.
Segmentation of the kidney and its tumor
Zhu, Kequan
Shen, Caomin
Peng, Yaxin
Ding, Xiaofeng
At present, computer-aided diagnosis and treatment has become a hot research direction. The segmentation of 3D medical images is an important part of computer-aided diagnosis and treatment. This paper uses a two-stage approach to achieve segmentation of kidney and kidney tumors from 3D CT image.
2019 Kidney Tumor Segmentation Challenge: Medical Image Segmentation with Two-Stage Process
Chen, Tung-I
Wu, Min-Sheng
Chang, Yu-Cheng
Lin, Jhih-Yuan
Since we are trying to deal with the medical images of real patients, the dataset are usually predominantly composed of ”normal” samples. The target classes only appear in a very small portion of the entire dataset, which leads to the so-called class imbalance problem. Besides, there is only a small percentage of foreground inside the ”abnormal” images. The great majority of background leads the significant detrimental effect on training. In such cases, model tends to focus on learning the dominant classes, leading to the poor prediction of minority class. However, the incorrect classification of pathological images can cause serious consequence in clinical practice.