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

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 Semiautomatic segmentation of longitudinal computed tomography images in a rat model of lung injury by surfactant depletion

2015 ◽  
Vol 118 (3) ◽  
pp. 377-385 ◽  
Author(s):  
Yi Xin ◽  
Gang Song ◽  
Maurizio Cereda ◽  
Stephen Kadlecek ◽  
Hooman Hamedani ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Image Segmentation ◽  
Lung Injury ◽  
Transformation Model ◽  
Reference Image ◽  
Quantitative Ct ◽  
Semiautomatic Segmentation ◽  
Computed Tomography Images ◽  
Lung Aeration ◽  
Surfactant Depletion

Quantitative analysis of computed tomography (CT) is essential to the study of acute lung injury. However, quantitative CT is made difficult by poor lung aeration, which complicates the critical step of image segmentation. To overcome this obstacle, this study sought to develop and validate a semiautomated, multilandmark, registration-based scheme for lung segmentation that is effective in conditions of poor aeration. Expiratory and inspiratory CT images were obtained in rats ( n = 8) with surfactant depletion of incremental severity to mimic worsening aeration. Trained operators manually delineated the images to provide a comparative landmark. Semiautomatic segmentation originated from a single, previously segmented reference image obtained at healthy baseline. Deformable registration of the target images (after surfactant depletion) was performed using the symmetric diffeomorphic transformation model with B-spline regularization. Registration used multiple landmarks (i.e., rib cage, spine, and lung parenchyma) to minimize the effect of poor aeration. Then target images were automatically segmented by applying the calculated transformation function to the reference image contour. Semiautomatically and manually segmented contours proved to be highly similar in all aeration conditions, including those characterized by more severe surfactant depletion and expiration. The Dice similarity coefficient was over 0.9 in most conditions, confirming high agreement, irrespective of poor aeration. Furthermore, CT density-based measurements of gas volume, tissue mass, and lung aeration distribution were minimally affected by the method of segmentation. Moving forward, multilandmark registration has the potential to streamline quantitative CT analysis by enabling semiautomatic image segmentation of lungs with a broad range of injury severity.

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.

scholarly journals Why Use Position Features in Liver Segmentation Performed by Convolutional Neural Network

2021 ◽  
Vol 12 ◽  
Author(s):  
Miroslav Jiřík ◽  
Filip Hácha ◽  
Ivan Gruber ◽  
Richard Pálek ◽  
Hynek Mírka ◽  
...  
Keyword(s):  
Neural Network ◽  
Computed Tomography ◽  
Image Segmentation ◽  
Convolutional Neural Network ◽  
Medical Image Segmentation ◽  
Position Estimation ◽  
The Body ◽  
Position Information ◽  
Computed Tomography Images ◽  
Distance Fields

Liver volumetry is an important tool in clinical practice. The calculation of liver volume is primarily based on Computed Tomography. Unfortunately, automatic segmentation algorithms based on handcrafted features tend to leak segmented objects into surrounding tissues like the heart or the spleen. Currently, convolutional neural networks are widely used in various applications of computer vision including image segmentation, while providing very promising results. In our work, we utilize robustly segmentable structures like the spine, body surface, and sagittal plane. They are used as key points for position estimation inside the body. The signed distance fields derived from these structures are calculated and used as an additional channel on the input of our convolutional neural network, to be more specific U-Net, which is widely used in medical image segmentation tasks. Our work shows that this additional position information improves the results of the segmentation. We test our approach in two experiments on two public datasets of Computed Tomography images. To evaluate the results, we use the Accuracy, the Hausdorff distance, and the Dice coefficient. Code is publicly available at: https://gitlab.com/hachaf/liver-segmentation.git.

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