scholarly journals Kidney and Kidney Tumor Segmentation Using Two- stage Convolutional Neural Network

2019 ◽  
Author(s):  
Junghyun Lee ◽  
Joonyoung Song ◽  
Serin Yang ◽  
Inhwa Han ◽  
Jong Chul Ye
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Segmentation ◽  
Kidney Tumor ◽  
Two Stage

scholarly journals Two-Stage Liver and Tumor Segmentation Algorithm Based on Convolutional Neural Network

Diagnostics ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 1806
Author(s):  
Lu Meng ◽  
Qianqian Zhang ◽  
Sihang Bu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Liver Tumors ◽  
Segmentation Algorithm ◽  
Tumor Segmentation ◽  
Liver Segmentation ◽  
Dice Coefficient ◽  
Long Distance ◽  
Two Stage ◽  
Public Data

The liver is an essential metabolic organ of the human body, and malignant liver tumors seriously affect and threaten human life. The segmentation algorithm for liver and liver tumors is one of the essential branches of computer-aided diagnosis. This paper proposed a two-stage liver and tumor segmentation algorithm based on the convolutional neural network (CNN). In the present study, we used two stages to segment the liver and tumors: liver localization and tumor segmentation. In the liver localization stage, the network segments the liver region, adopts the encoding–decoding structure and long-distance feature fusion operation, and utilizes the shallow features’ spatial information to improve liver identification. In the tumor segmentation stage, based on the liver segmentation results of the first two steps, a CNN model was designed to accurately identify the liver tumors by using the 2D image features and 3D spatial features of the CT image slices. At the same time, we use the attention mechanism to improve the segmentation performance of small liver tumors. The proposed algorithm was tested on the public data set Liver Tumor Segmentation Challenge (LiTS). The Dice coefficient of liver segmentation was 0.967, and the Dice coefficient of tumor segmentation was 0.725. The proposed algorithm can accurately segment the liver and liver tumors in CT images. Compared with other state-of-the-art algorithms, the segmentation results of the proposed algorithm rank the highest in the Dice coefficient.

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

scholarly journals Convolutional Neural Network for Kidney and kidney Tumor segmentation

2019 ◽  
Author(s):  
Vikas Kumar Anand ◽  
Pranav Aurangabadkar ◽  
Mahendra Khened ◽  
Ganapathy Krishnamurthi
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Tumor Segmentation ◽  
Kidney Tumor

scholarly journals A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

2020 ◽  
Vol 53 (2) ◽  
pp. 15374-15379
Author(s):  
Hu He ◽  
Xiaoyong Zhang ◽  
Fu Jiang ◽  
Chenglong Wang ◽  
Yingze Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection Method ◽  
Driver Fatigue ◽  
Two Stage ◽  
Fatigue Detection

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

Sign in / Sign up

Export Citation Format

Share Document