scholarly journals Segmentation of Skull-infiltrated Tumors Using ITK: Methods and Validation

2005 ◽  
Author(s):  
Aleksandra Popovic ◽  
Martin Engelhardt ◽  
Klaus Radermacher
Keyword(s):  
Computed Tomography ◽  
Hausdorff Distance ◽  
Distance Measure ◽  
Ct Images ◽  
Similarity Coefficient ◽  
Simulated Dataset ◽  
Dice Similarity Coefficient ◽  
Validation Metrics ◽  
Chi Squared ◽  
Sensitivity Specificity

Methods for segmentation of skull infiltrated tumors in Computed Tomography (CT) images using Insight Segmentation and Registration Toolkit ITK (www.itk.org) are presented. Pipelines of filters and algorithms from ITK are validated on the basis of different criteria: sensitivity, specificity, dice similarity coefficient, Chi-squared, and Hausdorff distance measure. The method to rate segmentation results in relation to validation metrics is presented together with analysis of importance of different goodness measures. Results for one simulated dataset and three patient are presented.

scholarly journals Detection and Severity Classification of COVID-19 in CT Images Using Deep Learning

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 893
Author(s):  
Yazan Qiblawey ◽  
Anas Tahir ◽  
Muhammad E. H. Chowdhury ◽  
Amith Khandakar ◽  
Serkan Kiranyaz ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Early Stage ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Lung Region ◽  
Computed Tomography Images ◽  
Feature Pyramid ◽  
Severity Levels

Detecting COVID-19 at an early stage is essential to reduce the mortality risk of the patients. In this study, a cascaded system is proposed to segment the lung, detect, localize, and quantify COVID-19 infections from computed tomography images. An extensive set of experiments were performed using Encoder–Decoder Convolutional Neural Networks (ED-CNNs), UNet, and Feature Pyramid Network (FPN), with different backbone (encoder) structures using the variants of DenseNet and ResNet. The conducted experiments for lung region segmentation showed a Dice Similarity Coefficient (DSC) of 97.19% and Intersection over Union (IoU) of 95.10% using U-Net model with the DenseNet 161 encoder. Furthermore, the proposed system achieved an elegant performance for COVID-19 infection segmentation with a DSC of 94.13% and IoU of 91.85% using the FPN with DenseNet201 encoder. The proposed system can reliably localize infections of various shapes and sizes, especially small infection regions, which are rarely considered in recent studies. Moreover, the proposed system achieved high COVID-19 detection performance with 99.64% sensitivity and 98.72% specificity. Finally, the system was able to discriminate between different severity levels of COVID-19 infection over a dataset of 1110 subjects with sensitivity values of 98.3%, 71.2%, 77.8%, and 100% for mild, moderate, severe, and critical, respectively.

scholarly journals Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning

2021 ◽  
Vol 20 ◽  
pp. 153303382110342
Author(s):  
Ruifen Cao ◽  
Xi Pei ◽  
Ning Ge ◽  
Chunhou Zheng
Keyword(s):  
Esophageal Cancer ◽  
Hausdorff Distance ◽  
Clinical Target Volume ◽  
Radical Surgery ◽  
Target Volume ◽  
Similarity Coefficient ◽  
Kappa Coefficient ◽  
Test Time ◽  
Dice Similarity Coefficient ◽  
Dilated Convolution

Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid training and testing. A dilated convolution module for extracting multiscale context features containing the original information on fine texture and boundaries is integrated into the U-network architecture to avoid information loss due to down-sampling and improve the segmentation accuracy. In addition, batch normalization is added to the deep dilated convolutional U-network for fast and stable convergence. In the present study, the training and validation loss tended to be stable after 40 training epochs. This deep dilated convolutional U-network model was able to segment the clinical target volume with an overall mean Dice similarity coefficient of 86.7% and a respective 95% Hausdorff distance of 37.4 mm, indicating reasonable volume overlap of the auto-segmented and manual contours. The mean Cohen kappa coefficient was 0.863, indicating that the deep dilated convolutional U-network was robust. Comparisons with the U-network and attention U-network showed that the overall performance of the deep dilated convolutional U-network was best for the Dice similarity coefficient, 95% Hausdorff distance, and Cohen kappa coefficient. The test time for segmentation of the clinical target volume was approximately 25 seconds per patient. This deep dilated convolutional U-network could be applied in the clinical setting to save time in delineation and improve the consistency of contouring.

3D Cascaded Convolutional Networks for Multi-vertebrae Segmentation

2020 ◽  
Vol 16 (3) ◽  
pp. 231-240
Author(s):  
Liu Xia ◽  
Liu Xiao ◽  
Gan Quan ◽  
Wang Bo
Keyword(s):  
Ct Images ◽  
Clinical Applications ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Segmentation Method ◽  
Convolutional Network ◽  
Improve Image Quality ◽  
Convolutional Networks ◽  
Spine Ct ◽  
Vertebrae Segmentation

Background: Automatic approach to vertebrae segmentation from computed tomography (CT) images is very important in clinical applications. As the intricate appearance and variable architecture of vertebrae across the population, cognate constructions in close vicinity, pathology, and the interconnection between vertebrae and ribs it is a challenge to propose a 3D automatic vertebrae CT image segmentation method. Objective: The purpose of this study was to propose an automatic multi-vertebrae segmentation method for spinal CT images. Methods: Firstly, CLAHE-Threshold-Expansion was preprocessed to improve image quality and reduce input voxel points. Then, 3D coarse segmentation fully convolutional network and cascaded finely segmentation convolutional neural network were used to complete multi-vertebrae segmentation and classification. Results: The results of this paper were compared with the other methods on the same datasets. Experimental results demonstrated that the Dice similarity coefficient (DSC) in this paper is 94.84%, higher than the V-net and 3D U-net. Conclusion: Method of this paper has certain advantages in automatically and accurately segmenting vertebrae regions of CT images. Due to the easy acquisition of spine CT images. It was proven to be more conducive to clinical application of treatment that uses our segmentation model to obtain vertebrae regions, combining with the subsequent 3D reconstruction and printing work.

scholarly journals Automatic Vertebral Body Segmentation using Semantic Segmentation

2019 ◽  
Vol 8 (4) ◽  
pp. 12163-12167
Keyword(s):  
Computed Tomography ◽  
Semantic Segmentation ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Challenging Problem ◽  
Average Accuracy ◽  
Body Segmentation ◽  
Vertebral Bodies ◽  
Segmentation Of Images ◽  
Main Components

Segmentation of vertebral bodies (VB) is a preliminary and useful step for the diagnosis of spine pathologies, deformations and fractures caused due to various reasons. We present a method to address this challenging problem of VB segmentation using a trending method – Semantic Segmentation (SS). The objective of semantic segmentation of images usually consisting of three main components - convolutions, downsampling, and upsampling layers is to mark every pixel of an image with a correlating class of what is being described. In this study, we developed a unique automatic semantic segmentation architecture to segment the VB from Computed Tomography (CT) images, and we compared our segmentation results with reference segmentations obtained by the experts. We evaluated the proposed method on a publicly available dataset and achieved an average accuracy of 94.16% and an average Dice Similarity Coefficient (DSC) of 93.51% for VB segmentation.

Semi-automatic measurement of intracranial hemorrhage growth on non-contrast CT

2019 ◽  
pp. 174749301989570 ◽  
Author(s):  
Kevin J Chung ◽  
Hulin Kuang ◽  
Alyssa Federico ◽  
Hyun Seok Choi ◽  
Linda Kasickova ◽  
...  
Keyword(s):  
Intracranial Hemorrhage ◽  
Volume Change ◽  
Intraclass Correlation ◽  
Ct Images ◽  
Similarity Coefficient ◽  
Dice Similarity Coefficient ◽  
Detectable Change ◽  
Minimum Detectable Change ◽  
Hemorrhage Volume ◽  
Contrast Ct

Background Manual segmentations of intracranial hemorrhage on non-contrast CT images are the gold-standard in measuring hematoma growth but are prone to rater variability. Aims We demonstrate that a convex optimization-based interactive segmentation approach can accurately and reliably measure intracranial hemorrhage growth. Methods Baseline and 16-h follow-up head non-contrast CT images of 46 subjects presenting with intracranial hemorrhage were selected randomly from the ANNEXA-4 trial imaging database. Three users semi-automatically segmented intracranial hemorrhage to measure hematoma volume for each timepoint using our proposed method. Segmentation accuracy was quantitatively evaluated compared to manual segmentations by using Dice similarity coefficient, Pearson correlation, and Bland–Altman analysis. Intra- and inter-rater reliability of the Dice similarity coefficient and intracranial hemorrhage volumes and volume change were assessed by the intraclass correlation coefficient and minimum detectable change. Results Among the three users, the mean Dice similarity coefficient, Pearson correlation, and mean difference ranged from 76.79% to 79.76%, 0.970 to 0.980 ( p < 0.001), and −1.5 to −0.4 ml, respectively, for all intracranial hemorrhage segmentations. Inter-rater intraclass correlation coefficients between the three users for Dice similarity coefficient and intracranial hemorrhage volume were 0.846 and 0.962, respectively, and the corresponding minimum detectable change was 2.51 ml. Inter-rater intraclass correlation coefficient for intracranial hemorrhage volume change ranged from 0.915 to 0.958 for each user compared to manual measurements, resulting in an minimum detectable change range of 2.14 to 4.26 ml. Conclusions We spatially and volumetrically validate a novel interactive segmentation method for delineating intracranial hemorrhage on head non-contrast CT images. Good spatial overlap, excellent volume correlation, and good repeatability suggest its usefulness for measuring intracranial hemorrhage volume and volume change on non-contrast CT images.

Segmentations of Liver and Hepatic Tumors from 3D Computed Tomography Abdominal Images

2014 ◽  
Vol 898 ◽  
pp. 684-687
Author(s):  
Yun Tao Wei ◽  
Yi Bing Zhou
Keyword(s):  
Computed Tomography ◽  
Liver Tissue ◽  
Processing Time ◽  
Ct Images ◽  
Graph Cut ◽  
Evaluation Framework ◽  
Dice Similarity Coefficient ◽  
Hepatic Tumors ◽  
Liver Segmentation ◽  
Fully Automatic

The segmentation of liver using computed tomography (CT) data has gained a lot of importance in the medical image processing field. In this paper, we present a survey on liver segmentation methods and techniques using CT images for liver segmentation. An adaptive initialization method was developed to produce fully automatic processing frameworks based on graph-cut and gradient flow active contour algorithms. This method was applied to abdominal Computed Tomography (CT) images for segmentation of liver tissue and hepatic tumors. Twenty-five anonymized datasets were randomly collected from several radiology centres without specific request on acquisition parameter settings nor patient clinical situation as inclusion criteria. Resulting automatic segmentations of liver tissue and tumors were compared to their reference standard delineations manually performed by a specialist. Segmentation accuracy has been assessed through the following evaluation framework: dice similarity coefficient, false negative ratio, false positive ratio and processing time. The implemented initialization method allows fully automatic segmentation leading to superior overall performances of graph-cut algorithm in terms of accuracy and processing time. The initialization method here presented resulted suitable and reliable for two different segmentation techniques and could be further extended.

scholarly journals Efficient Pre-Processing and Segmentation for Lung Cancer Detection Using Fused CT Images

Electronics ◽  
2021 ◽  
Vol 11 (1) ◽  
pp. 34
Author(s):  
Imran Nazir ◽  
Ihsan Ul Haq ◽  
Muhammad Mohsin Khan ◽  
Muhammad Bilal Qureshi ◽  
Hayat Ullah ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Fusion ◽  
Imaging Techniques ◽  
Image Database ◽  
Ct Images ◽  
Dice Similarity Coefficient ◽  
Fusion Technique ◽  
Lung Cancer Diagnosis ◽  
Image Fusion Technique ◽  
Sensitivity Specificity

Over the last two decades, radiologists have been using multi-view images to detect tumors. Computer Tomography (CT) imaging is considered as one of the reliable imaging techniques. Many medical-image-processing techniques have been developed to diagnoses lung cancer at early or later stages through CT images; however, it is still a big challenge to improve the accuracy and sensitivity of the algorithms. In this paper, we propose an algorithm based on image fusion for lung segmentation to optimize lung cancer diagnosis. The image fusion technique was developed through Laplacian Pyramid (LP) decomposition along with Adaptive Sparse Representation (ASR). The suggested fusion technique fragments medical images into different sizes using the LP. After that, the LP is used to fuse the four decomposed layers. For the evaluation purposes of the proposed technique, the Lungs Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) was used. The results showed that the Dice Similarity Coefficient (DSC) index of our proposed method was 0.9929, which is better than recently published results. Furthermore, the values of other evaluation parameters such as the sensitivity, specificity, and accuracy were 89%, 98% and 99%, respectively, which are also competitive with the recently published results.

scholarly journals Changes in Tumor Volumes and Spatial Locations Relative to Normal Tissues During Cervical Cancer Radiotherapy Assessed by Cone Beam Computed Tomography

2017 ◽  
Vol 16 (2) ◽  
pp. 246-252 ◽  
Author(s):  
Wenjuan Chen ◽  
Penggang Bai ◽  
Jianji Pan ◽  
Yuanji Xu ◽  
Kaiqiang Chen
Keyword(s):  
Computed Tomography ◽  
Cervical Cancer ◽  
Cone Beam Computed Tomography ◽  
Clinical Target Volume ◽  
Target Volume ◽  
Similarity Coefficient ◽  
Cone Beam ◽  
Dice Similarity Coefficient ◽  
Change Rate ◽  
Spatial Locations

Purpose: To assess changes in the volumes and spatial locations of tumors and surrounding organs by cone beam computed tomography during treatment for cervical cancer. Materials and Methods: Sixteen patients with cervical cancer had intensity-modulated radiotherapy and off-line cone beam computed tomography during chemotherapy and/or radiation therapy. The gross tumor volume (GTV-T) and clinical target volumes (CTVs) were contoured on the planning computed tomography and weekly cone beam computed tomography image, and changes in volumes and spatial locations were evaluated using the volume difference method and Dice similarity coefficients. Results: The GTV-T was 79.62 cm3 at prior treatment (0f) and then 20.86 cm3 at the end of external-beam chemoradiation. The clinical target volume changed slightly from 672.59 cm3 to 608.26 cm3, and the uterine volume (CTV-T) changed slightly from 83.72 cm3 to 80.23 cm3. There were significant differences in GTV-T and CTV-T among the different groups ( P < .001), but the clinical target volume was not significantly different in volume ( P > .05). The mean percent volume changes ranged from 23.05% to 70.85% for GTV-T, 4.71% to 6.78% for CTV-T, and 5.84% to 9.59% for clinical target volume, and the groups were significantly different ( P < .05). The Dice similarity coefficient of GTV-T decreased during the course of radiation therapy ( P < .001). In addition, there were significant differences in GTV-T among different groups ( P < .001), and changes in GTV-T correlated with the radiotherapy ( P < .001). There was a negative correlation between volume change rate (DV) and Dice similarity coefficient in the GTV-T and organs at risk ( r < 0; P < .05). Conclusion: The volume, volume change rate, and Dice similarity coefficient of GTV-T were all correlated with increase in radiation treatment. Significant variations in tumor regression and spatial location occurred during radiotherapy for cervical cancer. Adaptive radiotherapy approaches are needed to improve the treatment accuracy for cervical cancer.

Noninvasive Evaluation of Coronary Artery Bypass Grafts with 16-Slice Multidetector Computed Tomography

2012 ◽  
Vol 15 (1) ◽  
pp. 12 ◽  
Author(s):  
Levent Sahiner ◽  
Ali Oto ◽  
Kudret Aytemir ◽  
Tuncay Hazirolan ◽  
Musturay Karcaaltincaba ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Coronary Artery ◽  
Diagnostic Accuracy ◽  
Multidetector Computed Tomography ◽  
Coronary Artery Bypass ◽  
Predictive Value ◽  
Artery Bypass ◽  
Time Interval ◽  
Mdct Angiography ◽  
Sensitivity Specificity

<p><b>Background:</b> The aim of this study was to investigate the diagnostic accuracy of 16-slice multislice, multidetector computed tomography (MDCT) angiography for the evaluation of grafts in patients with coronary artery bypass grafting (CABG).</p><p><b>Methods:</b> Fifty-eight consecutive patients with CABG who underwent both MDCT and conventional invasive coronary angiography were included. The median time interval between the 2 procedures was 10 days (range, 1-32 days). The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of MDCT for the detection of occluded grafts were calculated. The accuracy of MDCT angiography for detecting significant stenoses in patent grafts and the evaluability of proximal and distal anastomoses were also investigated.</p><p><b>Results:</b> Optimal diagnostic images could not be obtained for only 3 (2%) of 153 grafts. Evaluation of the remaining 150 grafts revealed values for sensitivity, specificity, PPV, NPV, and diagnostic accuracy of the MDCT angiography procedure for the diagnosis of occluded grafts of 87%, 97%, 94%, 93%, and 92%, respectively. All of the proximal anastomoses were optimally visualized. In 4 (8%) of 50 patent arterial grafts, however, the distal anastomotic region could not be evaluated because of motion and surgical-clip artifacts. The accuracy of MDCT angiography for the detection of significant stenotic lesions was relatively low (the sensitivity, specificity, PPV, and NPV were 67%, 98%, 50%, and 99%, respectively). The number of significant lesions was insufficient to reach a reliable conclusion, however.</p><p><b>Conclusion:</b> Our study showed that MDCT angiography with 16-slice systems has acceptable diagnostic performance for the evaluation of coronary artery bypass graft patency.</p>

scholarly journals Value of preoperative computed tomography for meso-Rex bypass in children with extrahepatic portal vein obstruction

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Huiying Wu ◽  
Ning Zhou ◽  
Lianwei Lu ◽  
Xiwen Chen ◽  
Tao Liu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Portal Vein ◽  
Predictive Value ◽  
Extrahepatic Portal Vein Obstruction ◽  
Portal Vein Obstruction ◽  
Left Portal Vein ◽  
History Of ◽  
Associated Malformations ◽  
Sensitivity Specificity

Abstract Background Extrahepatic portal vein obstruction (EHPVO) is the most important cause of hematemesis in children. Intrahepatic left portal vein and superior mesenteric vein anastomosis, also known as meso-Rex bypass (MRB), is becoming the gold standard treatment for EHPVO. We analyzed the value of preoperative computed tomography (CT) in determining whether MRB is feasible in children with EHPVO. Results We retrieved data on 76 children with EHPVO (50 male, 26 female; median age, 5.9 years) who underwent MRB (n = 68) or the Warren procedure (n = 8) from 2013 to 2019 and retrospectively analyzed their clinical and CT characteristics. The Rex recess was categorized into four subtypes (types 1–4) depending on its diameter in CT images. Of all 76 children, 7.9% had a history of umbilical catheterization and 1.3% had leukemia. Sixteen patients (20 lesions) had associated malformations. A total of 72.4% of Rex recesses could be measured by CT, and their mean diameter was 3.5 ± 1.8 mm (range 0.6–10.5 mm). A type 1, 2, 3, and 4 Rex recess was present in 9.2%, 53.9%, 11.8%, and 25.0% of patients, respectively. MRB could be performed in patients with types 1, 2, and 3, but those with type 4 required further evaluation. The sensitivity, specificity, positive predictive value, negative predictive value, and diagnostic accuracy of CT were 100%, 83.8%, 42.1%, 100%, and 85.5%, respectively. Conclusions Among the four types of Rex recesses on CT angiography, types 1–3 allow for the performance of MRB.

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