scholarly journals Label Fusion Strategy Selection

2012 ◽  
Vol 2012 ◽  
pp. 1-13 ◽  
Author(s):  
Nicolas Robitaille ◽  
Simon Duchesne
Keyword(s):  
Medical Image ◽  
Three Dimensional ◽  
Simulated Data ◽  
Real Data ◽  
Medical Image Segmentation ◽  
Hybrid Technique ◽  
Strategy Selection ◽  
Label Fusion ◽  
Input Element ◽  
Almost All

Label fusion is used in medical image segmentation to combine several different labels of the same entity into a single discrete label, potentially more accurate, with respect to the exact, sought segmentation, than the best input element. Using simulated data, we compared three existing label fusion techniques—STAPLE, Voting, and Shape-Based Averaging (SBA)—and observed that none could be considered superior depending on the dissimilarity between the input elements. We thus developed an empirical, hybrid technique called SVS, which selects the most appropriate technique to apply based on this dissimilarity. We evaluated the label fusion strategies on two- and three-dimensional simulated data and showed that SVS is superior to any of the three existing methods examined. On real data, we used SVS to perform fusions of 10 segmentations of the hippocampus and amygdala in 78 subjects from the ICBM dataset. SVS selected SBA in almost all cases, which was the most appropriate method overall.

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

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.

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.

FUZZY INFORMATION GRANULATION ON BLOOD VESSEL EXTRACTION FROM 3D TOF MRA IMAGE

2000 ◽  
Vol 14 (04) ◽  
pp. 409-425 ◽  
Author(s):  
SYOJI KOBASHI ◽  
NAOTAKE KAMIURA ◽  
YUTAKA HATA ◽  
FUJIO MIYAWAKI
Keyword(s):  
Image Segmentation ◽  
Blood Vessel ◽  
Blood Vessels ◽  
Medical Image ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Volume Data ◽  
Fuzzy Information ◽  
Vessel Extraction ◽  
Fuzzy Information Granulation

This paper shows an application of fuzzy information granulation (fuzzy IG) to medical image segmentation. Fuzzy IG is to derive fuzzy granules from information. In the case of medical image segmentation, information and fuzzy granules correspond to an image taken from a medical scanner, and anatomical parts, namely region of interests (ROIs), respectively. The proposed method to granulate information is composed of volume quantization and fuzzy merging. Volume quantization is to gather similar neighboring voxels. The generated quanta are selectively merged according to degrees for pre-defined fuzzy models that represent anatomical knowledge of medical images. The proposed method was applied to blood vessel extraction from three-dimensional time-of-flight (TOF) magnetic resonance angiography (MRA) images of the brain. The volume data studied in this work is composed of about 100 contiguous and volumetric MRA images. According to the fuzzy IG concept, information correspond to the volume data, fuzzy granules corresponds to the blood vessels and fat. The qualitative evaluation by a physician was done for two- and three-dimensional images generated from the obtained blood vessels. The evaluation shows that the method can segment MRA volume data, and that fuzzy IG is applicable to, and suitable for medical image segmentation.

scholarly journals Accelerating 3D Medical Image Segmentation by Adaptive Small-Scale Target Localization

2021 ◽  
Vol 7 (2) ◽  
pp. 35
Author(s):  
Boris Shirokikh ◽  
Alexey Shevtsov ◽  
Alexandra Dalechina ◽  
Egor Krivov ◽  
Valery Kostjuchenko ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Graphics Processing Units ◽  
Medical Image ◽  
State Of The Art ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Small Scale ◽  
Processing Unit ◽  
Central Processing ◽  
Lung Cancer Diagnosis

The prevailing approach for three-dimensional (3D) medical image segmentation is to use convolutional networks. Recently, deep learning methods have achieved human-level performance in several important applied problems, such as volumetry for lung-cancer diagnosis or delineation for radiation therapy planning. However, state-of-the-art architectures, such as U-Net and DeepMedic, are computationally heavy and require workstations accelerated with graphics processing units for fast inference. However, scarce research has been conducted concerning enabling fast central processing unit computations for such networks. Our paper fills this gap. We propose a new segmentation method with a human-like technique to segment a 3D study. First, we analyze the image at a small scale to identify areas of interest and then process only relevant feature-map patches. Our method not only reduces the inference time from 10 min to 15 s but also preserves state-of-the-art segmentation quality, as we illustrate in the set of experiments with two large datasets.

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