Interactive Medical Image Segmentation Method with only Positive Label Examples

2014 ◽  
Vol 989-994 ◽  
pp. 1088-1092
Author(s):  
Chen Guang Zhang ◽  
Yan Zhang ◽  
Xia Huan Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Segmentation Method ◽  
High Quality ◽  
Neighbor Graph ◽  
Foreground Region ◽  
Training Examples ◽  
Positive Label ◽  
Negative Training

In this paper, a novel interactive medical image segmentation method called SMOPL is proposed. This method only needs marking some pixels on foreground region for segmentation. To do this, SMOPL characterize the inherent correlations among foreground and background pixels as Hilbert-Schmidt independence. By maximizing the independence and minimizing the smoothness of labels on instance neighbor graph simultaneously, SMOPL gets the sufficiently smooth confidences of both positive and negative classes in absence of negative training examples. Then a image segmentation can be obtained by assigning each pixel to the label for which the greatest confidence is calculated. Experiments on real-world medical images show that SMOPL is robust to get a high-quality segmentation with only positive label examples.

Level Set Segmentation Method in Medical Image Segmentation Research and Application

2012 ◽  
Vol 157-158 ◽  
pp. 1012-1015 ◽  
Author(s):  
Yu Miao ◽  
Wei Li Shi
Keyword(s):  
Image Segmentation ◽  
White Matter ◽  
Level Set ◽  
Medical Image ◽  
Region Of Interest ◽  
Medical Image Segmentation ◽  
The Other ◽  
Segmentation Method ◽  
Level Set Segmentation ◽  
Mri Image

Medical image segmentation can be divided into two categories: one is the region of interest (ROI) identification; the other is the description of the integrity and the extraction of interest region. The emergence of the level set method greatly promoted the development of medical image segmentation. This paper studies three different level set segmentation algorithm to achieve the effective segmentation for brain gray matter and white matter of MRI image.

scholarly journals A Novel Brain Image Segmentation Method Using an Improved 3D U-Net Model

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zhuqing Yang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Three Dimensional ◽  
Research Field ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Segmentation Method ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods ◽  
Three Dimensional Segmentation

Medical image segmentation (IS) is a research field in image processing. Deep learning methods are used to automatically segment organs, tissues, or tumor regions in medical images, which can assist doctors in diagnosing diseases. Since most IS models based on convolutional neural network (CNN) are two-dimensional models, they are not suitable for three-dimensional medical imaging. On the contrary, the three-dimensional segmentation model has problems such as complex network structure and large amount of calculation. Therefore, this study introduces the self-excited compressed dilated convolution (SECDC) module on the basis of the 3D U-Net network and proposes an improved 3D U-Net network model. In the SECDC module, the calculation amount of the model can be reduced by 1 × 1 × 1 convolution. Combining normal convolution and cavity convolution with an expansion rate of 2 can dig out the multiview features of the image. At the same time, the 3D squeeze-and-excitation (3D-SE) module can realize automatic learning of the importance of each layer. The experimental results on the BraTS2019 dataset show that the Dice coefficient and other indicators obtained by the model used in this paper indicate that the overall tumor can reach 0.87, the tumor core can reach 0.84, and the most difficult to segment enhanced tumor can reach 0.80. From the evaluation indicators, it can be analyzed that the improved 3D U-Net model used can greatly reduce the amount of data while achieving better segmentation results, and the model has better robustness. This model can meet the clinical needs of brain tumor segmentation methods.

scholarly journals STAMP: Simultaneous Training and Model Pruning for Low Data Regimes in Medical Image Segmentation

2021 ◽  
Author(s):  
Nicola K Dinsdale ◽  
Mark Jenkinson ◽  
Ana IL Namburete
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
High Quality ◽  
Vast Number ◽  
Large Numbers ◽  
Segmentation Algorithms ◽  
Simultaneous Training ◽  
Training And Pruning

Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be learned in the optimisation process. Here, we develop the STAMP algorithm to allow the simultaneous training and pruning of a UNet architecture for medical image segmentation with targeted channelwise dropout to make the network robust to the pruning. We demonstrate the technique across segmentation tasks and imaging modalities. It is then shown that, through online pruning, we are able to train networks to have much higher performance than the equivalent standard UNet models while reducing their size by more than 85% in terms of parameters. This has the potential to allow networks to be directly trained on datasets where very low numbers of labels are available.

scholarly journals Render U-Net: A Unique Perspective on Render to Explore Accurate Medical Image Segmentation

2020 ◽  
Vol 10 (18) ◽  
pp. 6439
Author(s):  
Chen Li ◽  
Wei Chen ◽  
Yusong Tan
Keyword(s):  
Image Segmentation ◽  
Mortality Rate ◽  
High Mortality ◽  
Medical Image ◽  
Sampling Method ◽  
Medical Image Segmentation ◽  
Natural Image ◽  
High Quality ◽  
Public Datasets ◽  
Segmentation Models

Organ lesions have a high mortality rate, and pose a serious threat to people’s lives. Segmenting organs accurately is helpful for doctors to diagnose. There is a demand for the advanced segmentation model for medical images. However, most segmentation models directly migrated from natural image segmentation models. These models usually ignore the importance of the boundary. To solve this difficulty, in this paper, we provided a unique perspective on rendering to explore accurate medical image segmentation. We adapt a subdivision-based point-sampling method to get high-quality boundaries. In addition, we integrated the attention mechanism and nested U-Net architecture into the proposed network Render U-Net.Render U-Net was evaluated on three public datasets, including LiTS, CHAOS, and DSB. This model obtained the best performance on five medical image segmentation tasks.

scholarly journals Medical Image Segmentation Algorithm Based on Feedback Mechanism CNN

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Feng-Ping An ◽  
Zhi-Wen Liu
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image ◽  
Rapid Development ◽  
Feedback Mechanism ◽  
Medical Image Segmentation ◽  
Segmentation Algorithm ◽  
Segmentation Method ◽  
Image Segmentation Algorithm

With the development of computer vision and image segmentation technology, medical image segmentation and recognition technology has become an important part of computer-aided diagnosis. The traditional image segmentation method relies on artificial means to extract and select information such as edges, colors, and textures in the image. It not only consumes considerable energy resources and people’s time but also requires certain expertise to obtain useful feature information, which no longer meets the practical application requirements of medical image segmentation and recognition. As an efficient image segmentation method, convolutional neural networks (CNNs) have been widely promoted and applied in the field of medical image segmentation. However, CNNs that rely on simple feedforward methods have not met the actual needs of the rapid development of the medical field. Thus, this paper is inspired by the feedback mechanism of the human visual cortex, and an effective feedback mechanism calculation model and operation framework is proposed, and the feedback optimization problem is presented. A new feedback convolutional neural network algorithm based on neuron screening and neuron visual information recovery is constructed. So, a medical image segmentation algorithm based on a feedback mechanism convolutional neural network is proposed. The basic idea is as follows: The model for obtaining an initial region with the segmented medical image classifies the pixel block samples in the segmented image. Then, the initial results are optimized by threshold segmentation and morphological methods to obtain accurate medical image segmentation results. Experiments show that the proposed segmentation method has not only high segmentation accuracy but also extremely high adaptive segmentation ability for various medical images. The research in this paper provides a new perspective for medical image segmentation research. It is a new attempt to explore more advanced intelligent medical image segmentation methods. It also provides technical approaches and methods for further development and improvement of adaptive medical image segmentation technology.

A New Medical Image Segmentation Method Based on Chan-Vese Model

2014 ◽  
Vol 513-517 ◽  
pp. 3750-3756 ◽  
Author(s):  
Yuan Zheng Ma ◽  
Jia Xin Chen
Keyword(s):  
Image Segmentation ◽  
Mathematical Morphology ◽  
Level Sets ◽  
Medical Image ◽  
Medical Image Segmentation ◽  
Experimental Results ◽  
Segmentation Method ◽  
Accuracy Requirement ◽  
Segmentation Accuracy ◽  
Segmentation Problem

The traditional segmentation method for medical image segmentation is difficult to achieve the accuracy requirement, and when the edges of the image are blurred, it will occurs incomplete segmentation problem, in order to solve this problem, we propose a medical image segmentation method which based on Chan-Vese model and mathematical morphology. The method integrates Chan-Vese model, mathematical morphology, composite multiphase level sets segmentation algorithm, first, through iterative etching operation to extract the outline of the medical image, and then the medical image is segmented by the Chan-Vese model based on the complex multiphase level sets, finally the medical image image is dilated iteratively by using morphological dilation to restore the image. The experimental results and analysis show that, this method improves the multi-region segmentation accuracy during the segmentation of medical image and solves the problem of incomplete segmentation.

Sign in / Sign up

Export Citation Format

Share Document