scholarly journals Anatomically Based Geometric Modelling Using Medical Image Data: Methods and Programs

2015 ◽  
Vol 9 (1) ◽  
pp. 126-131
Author(s):  
Monan Wang ◽  
Lei Sun ◽  
Yuming Liu
Keyword(s):  
Geometric Modeling ◽  
Medical Image ◽  
Structural Information ◽  
Geometric Model ◽  
Three Dimensional ◽  
Image Data ◽  
Region Growing ◽  
Reconstruction Algorithm ◽  
Medical Image Segmentation ◽  
Image Browsing

Geometric modeling software that can realize two-dimensional medical image browsing, preprocessing, and three-dimensional (3D) reconstruction is designed for modeling human organs. This software performs medical image segmentation using a method that combines the region growing and the interactive segmentation methods. The Marching Cubes surface reconstruction algorithm is used to obtain a 3D geometric model. The program is compiled using Visual Studio 2010. The software is employed to obtain the geometric model of the human femur, hipbone, and muscle. The geometric modeling results can accurately express the structural information of the skeleton and muscle.

Three-dimensional Medical Image Segmentation with SE-VNet Neural Networks

2021 ◽  
Author(s):  
Sheng Lu ◽  
Jungang Han ◽  
Jiantao Li ◽  
Liyang Zhu ◽  
Jiewei Jiang ◽  
...  
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Segmentation

scholarly journals Mixup (Sample Pairing) Can Improve the Performance of Deep Segmentation Networks

2021 ◽  
Vol 12 (1) ◽  
pp. 29-39
Author(s):  
Lars J. Isaksson ◽  
Paul Summers ◽  
Sara Raimondi ◽  
Sara Gandini ◽  
Abhir Bhalerao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Data Augmentation ◽  
Image Data ◽  
Magnetic Resonance Images ◽  
Medical Image Segmentation ◽  
Dice Similarity Coefficient ◽  
Surface Distance ◽  
Experienced Radiologist ◽  
Generalization Problem

Abstract Researchers address the generalization problem of deep image processing networks mainly through extensive use of data augmentation techniques such as random flips, rotations, and deformations. A data augmentation technique called mixup, which constructs virtual training samples from convex combinations of inputs, was recently proposed for deep classification networks. The algorithm contributed to increased performance on classification in a variety of datasets, but so far has not been evaluated for image segmentation tasks. In this paper, we tested whether the mixup algorithm can improve the generalization performance of deep segmentation networks for medical image data. We trained a standard U-net architecture to segment the prostate in 100 T2-weighted 3D magnetic resonance images from prostate cancer patients, and compared the results with and without mixup in terms of Dice similarity coefficient and mean surface distance from a reference segmentation made by an experienced radiologist. Our results suggest that mixup offers a statistically significant boost in performance compared to non-mixup training, leading to up to 1.9% increase in Dice and a 10.9% decrease in surface distance. The mixup algorithm may thus offer an important aid for medical image segmentation applications, which are typically limited by severe data scarcity.

Construction of Analysis-Suitable Vascular Models Using Axis-Aligned Polycubes

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Adam R. Updegrove ◽  
Shawn C. Shadden ◽  
Nathan M. Wilson
Keyword(s):  
Medical Image ◽  
Geometric Model ◽  
Image Data ◽  
Boundary Representation ◽  
Patient Specific ◽  
Surface Model ◽  
Suitable Model ◽  
Computational Domain ◽  
Discrete Surface ◽  
Medical Image Data

Image-based modeling is an active and growing area of biomedical research that utilizes medical imaging to create patient-specific simulations of physiological function. Under this paradigm, anatomical structures are segmented from a volumetric image, creating a geometric model that serves as a computational domain for physics-based modeling. A common application is the segmentation of cardiovascular structures to numerically model blood flow or tissue mechanics. The segmentation of medical image data typically results in a discrete boundary representation (surface mesh) of the segmented structure. However, it is often desirable to have an analytic representation of the model, which facilitates systematic manipulation. For example, the model then becomes easier to union with a medical device, or the geometry can be virtually altered to test or optimize a surgery. Furthermore, to employ increasingly popular isogeometric analysis (IGA) methods, the parameterization must be analysis suitable. Converting a discrete surface model to an analysis-suitable model remains a challenge, especially for complex branched structures commonly encountered in cardiovascular modeling. To address this challenge, we present a framework to convert discrete surface models of vascular geometries derived from medical image data into analysis-suitable nonuniform rational B-splines (NURBS) representation. This is achieved by decomposing the vascular geometry into a polycube structure that can be used to form a globally valid parameterization. We provide several practical examples and demonstrate the accuracy of the methods by quantifying the fidelity of the parameterization with respect to the input geometry.

Preprocessing of Medical Image Data for Three-Dimensional Bioprinted Customized-Neural-Scaffolds

2019 ◽  
Vol 25 (7) ◽  
pp. 401-410
Author(s):  
Kate Da Silva ◽  
Pradeep Kumar ◽  
Yahya E. Choonara ◽  
Lisa C. du Toit ◽  
Viness Pillay
Keyword(s):  
Medical Image ◽  
Three Dimensional ◽  
Image Data ◽  
Medical Image Data

CS-LBF Improved Model for 3D Medical Image Segmentation

2013 ◽  
Vol 303-306 ◽  
pp. 2272-2279 ◽  
Author(s):  
Wen Cang Zhao ◽  
Jun Bo Zhang
Keyword(s):  
Image Segmentation ◽  
Medical Image ◽  
Contrast Ratio ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Low Contrast ◽  
Shape Constraint ◽  
Local Binary Fitting Model ◽  
Improved Model ◽  
Fitting Model

This paper presents an algorithm for three-dimensional medical image segmentation based on the Contrast and Shape Constrained Local Binary Fitting improved model. Due to Local Binary Fitting model is sensitive to initialization and easy to fall into local extreme value, the new algorithm adds contrast constraint term to the Local Binary Fitting model, aiming at solving the common existed problem of inconsistent brightness and low contrast ratio. Adding shape constraint term can improve the original Local Binary Fitting model by constructing shape constraint energy field around the average shape by the level set method to deal with the leak of deformation curve. In order to promote the speed of model evolution, the kernel function is simplified. Two-dimensional Contrast and Shape Constrained Local Binary Fitting model is then extended to three-dimensional and a three-dimensional dental pulp image is segmented. Experimental results show that the segmentation accuracy, the connection degree and the efficiency of the new method are greatly improved compared to original LBF model.

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.

Methods and Applications for Segmenting 3D Medical Image Data

2011 ◽  
pp. 303-320
Author(s):  
Hong Shen
Keyword(s):  
Computer Vision ◽  
Image Segmentation ◽  
General Problem ◽  
Real World ◽  
Medical Image ◽  
Image Data ◽  
Medical Image Segmentation ◽  
Knowledge Based ◽  
Typical Sample ◽  
Medical Image Data

In this chapter, we will give an intuitive introduction to the general problem of 3D medical image segmentation. We will give an overview of the popular and relevant methods that may be applicable, with a discussion about their advantages and limits. Specifically, we will discuss the issue of incorporating prior knowledge into the segmentation of anatomic structures and describe in detail the concept and issues of knowledge-based segmentation. Typical sample applications will accompany the discussions throughout this chapter. We hope this will help an application developer to improve insights in the understanding and application of various computer vision approaches to solve real-world problems of medical image segmentation.

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