On the Design of Active Contours for Medical Image Segmentation

2003 ◽  
Vol 42 (01) ◽  
pp. 89-98 ◽  
Author(s):  
J. Bredno ◽  
K. Spitzer ◽  
T.M. Lehmann
Keyword(s):  
Medical Image ◽  
Object Representation ◽  
Active Contours ◽  
General Purpose ◽  
Medical Image Segmentation ◽  
Snake Model ◽  
Essential Information ◽  
Applied Model ◽  
Robust Segmentation ◽  
Segmentation Methods

Summary Objectives: To provide a comprehensive bottom-up categorization of model-based segmentation techniques that allows to select, implement, and apply well-suited active contour models for segmentation of medical images, where major challenges are the high variability in shape and appearance of objects, noise, artifacts, partial occlusions of objects, and the required reliability and correctness of results. Methods: We consider the general purpose of segmentation, the dimension of images, the object representation within the model, image and contour influences, as well as the solution and the parameter selection of the model. Potentials and limits are characterized for all instances in each category providing essential information for the application of active contours to various purposes in medical image processing. Based on prolaps surgery planning, we exemplify the use of the scheme to successfully design robust 3D-segmentation. Results: The construction scheme allows to design robust segmentation methods, which, in particular, should avoid any gaps of dimension. Such gaps result from different image domains and value ranges with respect to the applied model domain and the dimension of relevant subsets for image influences, respectively. Conclusions: A general segmentation procedure with sufficient robustness for medical applications is still missing. It is shown that in almost every category, novel techniques are available to improve the initial snake model, which was introduced in 1987.

scholarly journals Interactive Medical Image Segmentation Using Snake and Multiscale Curve Editing

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Wu Zhou ◽  
Yaoqin Xie
Keyword(s):  
Image Segmentation ◽  
Active Contour ◽  
Medical Image ◽  
Surgical Planning ◽  
Active Contour Model ◽  
Clinical Applications ◽  
Medical Image Segmentation ◽  
Interactive Segmentation ◽  
Snake Model ◽  
Segmentation Methods

Image segmentation is typically applied to locate objects and boundaries, and it is an essential process that supports medical diagnosis, surgical planning, and treatments in medical applications. Generally, this process is done by clinicians manually, which may be accurate but tedious and very time consuming. To facilitate the process, numerous interactive segmentation methods have been proposed that allow the user to intervene in the process of segmentation by incorporating prior knowledge, validating results and correcting errors. The accurate segmentation results can potentially be obtained by such user-interactive process. In this work, we propose a novel framework of interactive medical image segmentation for clinical applications, which combines digital curves and the active contour model to obtain promising results. It allows clinicians to quickly revise or improve contours by simple mouse actions. Meanwhile, the snake model becomes feasible and practical in clinical applications. Experimental results demonstrate the effectiveness of the proposed method for medical images in clinical applications.

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.

Medical segmentation with generative adversarial semi-supervised network

2021 ◽  
Author(s):  
Chuchen Li ◽  
Huafeng Liu
Keyword(s):  
Medical Image ◽  
Large Scale ◽  
Auxiliary Information ◽  
Medical Image Segmentation ◽  
Training Data ◽  
Feature Mapping ◽  
Adversarial Learning ◽  
Segmentation Methods ◽  
Medical Segmentation ◽  
Self Learning

Abstract Recent medical image segmentation methods heavily rely on large-scale training data and high-quality annotations. However, these resources are hard to obtain due to the limitation of medical images and professional annotators. How to utilize limited annotations and maintain the performance is an essential yet challenging problem. In this paper, we try to tackle this problem in a self-learning manner by proposing a Generative Adversarial Semi-supervised Network (GASNet). We use limited annotated images as main supervision signals, and the unlabeled images are manipulated as extra auxiliary information to improve the performance. More specifically, we modulate a segmentation network as a generator to produce pseudo labels for unlabeled images. To make the generator robust, we train an uncertainty discriminator with generative adversarial learning to determine the reliability of the pseudo labels. To further ensure dependability, we apply feature mapping loss to obtain statistic distribution consistency between the generated labels and the real labels. Then the verified pseudo labels are used to optimize the generator in a self-learning manner. We validate the effectiveness of the proposed method on right ventricle dataset, Sunnybrook dataset, STACOM, ISIC dataset, and Kaggle lung dataset. We obtain 0.8402 to 0.9121, 0.8103 to 0.9094, 0.9435 to 0.9724, 0.8635 to 0.886, and 0.9697 to 0.9885 dice coefficient with 1/8 to 1/2 proportion of densely annotated labels, respectively. The improvements are up to 28.6 points higher than the corresponding fully supervised baseline.

Proposal of Fast Implicit Level Set Scheme for Medical Image Segmentation Using the Chan and Vese Model

2011 ◽  
Vol 103 ◽  
pp. 695-699 ◽  
Author(s):  
Hui Min Lu ◽  
Serikawa Seiichi ◽  
Yu Jie Li ◽  
Li Feng Zhang ◽  
Shi Yuan Yang ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Level Set ◽  
Social Life ◽  
Medical Image ◽  
High Speed ◽  
Active Contours ◽  
Operator Splitting ◽  
Medical Image Segmentation ◽  
Proposed Model ◽  
The Social

People living in the information age, are more and more attention to their lives. It is also said, social life is more important in present and future. The social life contains three fields. In this paper, we propose a new model for active contours to detect objects in a given medical image, in order to facilitate people to have medical treatment. The proposed method is based on techniques of piecewise constant and piecewise smooths Chan-Vese Model, semi-implicit additive operator splitting (AOS) scheme for image segmentation. Different from traditional models, our model uses the level set which are corresponding to ordinary differential equation (ODE). Our model has more improved characteristics than traditional models, such as: less sensibility of noise; unnecessary of re-initialization and high speed by the simplified ordinary differential function. Finally, we validate the proposed model by numerical synthetic and real images. The experimental results demonstrate that our model is at least two times more efficient than the widely used methods.

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