Creeping Contours: A Multilabel Image Segmentation Method for Extracting Boundary Surfaces of Parts in Volumetric Images

2011 ◽  
Vol 11 (1) ◽  
Author(s):  
Haitham Shammaa ◽  
Hiromasa Suzuki ◽  
Yutaka Ohtake
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Initial Contour ◽  
Segmentation Method ◽  
Mechanical Parts ◽  
Computed Tomography Images ◽  
Volumetric Images ◽  
Speed Function ◽  
Boundary Surfaces ◽  
Grayscale Images

In this work, we introduce a method named creeping contours for image segmentation into component parts for the purpose of extracting the boundary surfaces of these parts. Creeping contours are contours that expand following a speed function defined by the gradient and curvature at contour points, starting from an initial contour position defined either manually or automatically. Contours in the image creep simultaneously at different speeds, while labels are assigned to contour pixels by the defined creeping condition. We also demonstrate the effectiveness of the proposed method by segmenting 2D grayscale images and 3D volumetric computed tomography images of mechanical parts into multiple segments and generating the boundary surfaces of these parts.

Optimal image segmentation protocol for 3D printing of aortic dissection through open-source software

2021 ◽  
Author(s):  
Chia-An Wu ◽  
Andrew Squelch ◽  
Zhonghua Sun
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
3D Printing ◽  
Aortic Dissection ◽  
Open Source ◽  
Open Source Software ◽  
Type B ◽  
Manual Intervention ◽  
Computed Tomography Images ◽  
Computed Tomography Image

Aim: To determine the optimal image segmentation protocol that minimizes the amount of manual intervention and correction required while extracting 3D model geometries suitable for 3D printing of aortic dissection (AD) using open-source software. Materials & methods: Computed tomography images of two type B AD cases were selected with images segmented using a 3D Slicer to create a hollow model containing the aortic wall and intimal tear. A workflow composed of filters, lumen extraction and outer surface creation was developed. Results & conclusion: The average difference in measurements at 14 anatomical locations between the Standard Tessellation Language file and the computed tomography image for cases 1 and 2 were 0.29 and 0.32 mm, respectively. The workflow for the image segmentation of type B AD was able to produce a high-accuracy 3D-printed model in a short time through open-source software.

Image segmentation of nanoscale Zernike phase contrast X-ray computed tomography images

2015 ◽  
Vol 117 (18) ◽  
pp. 183102 ◽  
Author(s):  
Arjun S. Kumar ◽  
Pratiti Mandal ◽  
Yongjie Zhang ◽  
Shawn Litster
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Phase Contrast ◽  
X Ray ◽  
Computed Tomography Images ◽  
X Ray Computed

scholarly journals Review methods for image segmentation from computed tomography images

2014 ◽  
Author(s):  
Nurwahidah Mamat ◽  
Wan Eny Zarina Wan Abdul Rahman ◽  
Shaharuddin Cik Soh ◽  
Rozi Mahmud
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Computed Tomography Images

scholarly journals Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm

2021 ◽  
Vol 37 (6-WIT) ◽  
Author(s):  
Feng Zhu ◽  
Bo Zhang
Keyword(s):  
Computed Tomography ◽  
Lung Cancer ◽  
Image Segmentation ◽  
Pulmonary Tuberculosis ◽  
Clinical Characteristics ◽  
Ct Image ◽  
Segmentation Method ◽  
Tuberculosis Patients ◽  
Cancer Group ◽  
Image Segmentation Algorithm

Objective: We used U-shaped convolutional neural network (U_Net) multi-constraint image segmentation method to compare the diagnosis and imaging characteristics of tuberculosis and tuberculosis with lung cancer patients with Computed Tomography (CT). Methods: We selected 160 patients with tuberculosis from the severity scoring (SVR) task is provided by Image CLEF Tuberculosis 2019. According to the type of diagnosed disease, they were divided into tuberculosis combined with lung cancer group and others group, all patients were given chest CT scan, and the clinical manifestations, CT characteristics, and initial suspected diagnosis and missed diagnosis of different tumor diameters were observed and compared between the two groups. Results: There were more patients with hemoptysis and hoarseness in pulmonary tuberculosis combined with lung cancer group than in the pulmonary others group (P<0.05), and the other symptoms were not significantly different (P>0.05). Tuberculosis combined with lung cancer group had fewer signs of calcification, streak shadow, speckle shadow, and cavitation than others group; however, tuberculosis combined with lung cancer group had more patients with mass shadow, lobular sign, spines sign, burr sign and vacuole sign than others group. Conclusion: The symptoms of hemoptysis and hoarseness in pulmonary tuberculosis patients need to consider whether the disease has progressed and the possibility of lung cancer lesions. CT imaging of pulmonary tuberculosis patients with lung cancer usually shows mass shadows, lobular signs, spines signs, burr signs, and vacuoles signs. It can be used as the basis for its diagnosis. Simultaneously, the U-Net-based segmentation method can effectively segment the lung parenchymal region, and the algorithm is better than traditional algorithms. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 How to cite this:Zhu F, Zhang B. Analysis of the Clinical Characteristics of Tuberculosis Patients based on Multi-Constrained Computed Tomography (CT) Image Segmentation Algorithm. Pak J Med Sci. 2021;37(6):1705-1709. doi: https://doi.org/10.12669/pjms.37.6-WIT.4795 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

scholarly journals Multi-Projection Segmentation on Dental Cone Beam Computed Tomography Images Using Level Set Method

2021 ◽  
Vol 5 (2) ◽  
pp. 130-139
Author(s):  
Fahmi Syuhada ◽  
Rarasmaya Indraswari ◽  
Agus Zainal Arifin ◽  
Dini Adni Navastara
Keyword(s):  
Computed Tomography ◽  
Level Set ◽  
Cone Beam Computed Tomography ◽  
Cone Beam ◽  
Projection Data ◽  
Segmentation Method ◽  
Computed Tomography Images ◽  
Level Set Segmentation ◽  
Level Method ◽  
Sensitivity Specificity

Segmentation of dental Cone-beam computed tomography (CBCT) images based on Boundary Tracking has been widely used in recent decades. Generally, the process only uses axial projection data of CBCT where the slices image that representing the tip of the tooth object have decreased in contrast which impact to difficult to distinguish with background or other elements. In this paper we propose the multi-projection segmentation method by combining the level set segmentation result on three projections to detect the tooth object more optimally. Multiprojection is performed by decomposing CBCT data which produces three projections called axial, sagittal and coronal projections. Then, the segmentation based on the set level method is implemented on the slices image in the three projections. The results of the three projections are combined to get the final result of this method. This proposed method obtains evaluation results of accuracy, sensitivity, specificity with values of 97.18%, 88.62%, and 97.61%, respectively.

A Robust Deep Learning Segmentation Method for Hematoma Volumetric Detection in Intracerebral Hemorrhage

Stroke ◽  
2021 ◽  
Author(s):  
Nannan Yu ◽  
He Yu ◽  
Haonan Li ◽  
Nannan Ma ◽  
Chunai Hu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Deep Learning ◽  
Intracerebral Hemorrhage ◽  
Epidural Hematoma ◽  
Contour Method ◽  
Data Sets ◽  
Segmentation Method ◽  
Data Set ◽  
Testing Data

Background and Purpose: Hematoma volume (HV) is a significant diagnosis for determining the clinical stage and therapeutic approach for intracerebral hemorrhage (ICH). The aim of this study is to develop a robust deep learning segmentation method for the fast and accurate HV analysis using computed tomography. Methods: A novel dimension reduction UNet (DR-UNet) model was developed for computed tomography image segmentation and HV measurement. Two data sets, 512 ICH patients with 12 568 computed tomography slices in the retrospective data set and 50 ICH patients with 1257 slices in the prospective data set, were used for network training, validation, and internal and external testing. Moreover, 13 irregular hematoma cases, 11 subdural and epidural hematoma cases, and 50 different HV cases into 3 groups (<30, 30–60, and >60 mL) were selected to further evaluate the robustness of DR-UNet. The image segmentation performance of DR-UNet was compared with those of UNet, the fuzzy clustering method, and the active contour method. The HV measurement performance was compared using DR-UNet, UNet, and the Coniglobus formula method. Results: Using DR-UNet, the segmentation model achieved a performance similar to that of expert clinicians in 2 independent test data sets containing internal testing data (Dice of 0.861±0.139) and external testing data (Dice of 0.874±0.130). The HV measurement derived from DR-UNet was strongly correlated with that from manual segmentation (R 2 =0.9979; P <0.0001). In the irregularly shaped hematoma group and the subdural and epidural hematoma group, DR-UNet was more robust than UNet in both hematoma segmentation and HV measurement. There is no statistical significance in segmentation accuracy among 3 different HV groups. Conclusions: DR-UNet can segment hematomas from the computed tomography scans of ICH patients and quantify the HV with better accuracy and greater efficiency than the main existing methods and with similar performance to expert clinicians. Due to robust performance and stable segmentation on different ICHs, DR-UNet could facilitate the development of deep learning systems for a variety of clinical applications.

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