scholarly journals Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images

2017 ◽  
Author(s):  
◽  
D. E. Rodríguez-Obregón
Keyword(s):  
Computed Tomography ◽  
Clinical Practice ◽  
Pulmonary Fibrosis ◽  
Data Base ◽  
Support System ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Ct Imaging ◽  
Segmentation Algorithm ◽  
Clinical Expert

A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis region with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitativeestimation of fibrosis region.

scholarly journals Support System for Semiautomatic Quantification of Pulmonary Fibrosis in CT Images

2017 ◽  
Author(s):  
◽  
D. E. Rodríguez-Obregón
Keyword(s):  
Computed Tomography ◽  
Clinical Practice ◽  
Pulmonary Fibrosis ◽  
Data Base ◽  
Support System ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Ct Imaging ◽  
Segmentation Algorithm ◽  
Clinical Expert

A method to estimate the pulmonary fibrosis in computed tomography (CT) imaging is presented. A semi-automatic segmentation algorithm based on the Chan-Vese method was used. The proposed method shows a similar fibrosis region with respect to clinical expert. However, the results need to be validated in a bigger data base. The proposed method approximates a fibrosis percentage that allows to achieve this procedure easily in order to support its implementation in the clinical practice minimizing the clinical expert subjectivity and generating a quantitativeestimation of fibrosis region.

scholarly journals Automatic Segmentation Using a Hybrid Dense Network Integrated With an 3D-Atrous Spatial Pyramid Pooling Module for Computed Tomography (CT) Imaging

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 169794-169803
Author(s):  
Abdul Qayyum ◽  
Iftikhar Ahmad ◽  
Wajid Mumtaz ◽  
Madini O. Alassafi ◽  
Rayed Alghamdi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Automatic Segmentation ◽  
Ct Imaging ◽  
Dense Network ◽  
Spatial Pyramid Pooling ◽  
Spatial Pyramid

scholarly journals Standardised 3D-CT Lung Volumes for Patients with Idiopathic Pulmonary Fibrosis

2021 ◽  
Author(s):  
Yuko Tanaka ◽  
Yuzo Suzuki ◽  
Hirotsugu Hasegawa ◽  
Koshi Yokomura ◽  
Atsuki Fukada ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Pulmonary Function Tests ◽  
Three Dimensional ◽  
Ct Images ◽  
3D Ct ◽  
Pulmonary Physiology ◽  
Lung Physiology ◽  
Lower Lung

Abstract Background: The assessment of lung physiology via pulmonary function tests (PFTs) is essential for patients with idiopathic pulmonary fibrosis (IPF). However, PFTs require active participation, which can be challenging for patients with severe respiratory failure, such as during acute exacerbations (AE) of IPF. Recently advances enabled to re-construct of 3-dimensional computed-tomography (3D-CT) images. Methods: This is a retrospective multi-center cohort study. This study established a standardisation method and quantitative analysis of lung volume (LV) based on anthropometry using three-dimensional computed tomography (3D-CT) images. The standardised 3D-CT LV in patients with IPF at diagnosis (n=140) and during AE (cohort1; n=61 and cohort2; n=50) and those of controls (n=53) were measured. Results: The standardised 3D-CT LVs at IPF diagnosis were less than those of control patients, especially in the lower lung lobes. The standardised 3D-CT LVs were correlated with forced vital capacity (FVC) and validated using the modified Gender-Age-Physiology (GAP) index. The standardised 3D-CT LVs at IPF diagnosis were independently associated with prognosis. During AE, PFTs were difficult to perform, 3D-CT analyses revealed reduced lung capacity in both the upper and lower lobes compared to those obtained at diagnosis. Lower standardised 3D-CT LVs during AE were independently associated with worse outcomes in independent two cohorts. Particularly, volume loss in the upper lobe at AE had prognostic values.Conclusion: A novel image quantification method for assessing pulmonary physiology using standardised 3D-CT-derived LVs was developed. This method successfully predicts mortality in patients with IPF and AE of IPF, and may be a useful alternative to PFTs when PFTs cannot be performed.

Correlation of displacement vector fields calculated by different deformable image registration algorithms with motion parameters in helical, axial and cone beam CT imaging

2019 ◽  
Vol 19 (3) ◽  
pp. 219-225
Author(s):  
Nesreen Alsbou ◽  
Salahuddin Ahmad ◽  
Imad Ali
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Vector Fields ◽  
Displacement Vector ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Ct Imaging ◽  
Cone Beam ◽  
Helical Computed Tomography ◽  
Motion Parameters

AbstractAim:The purpose of this study is to investigate quantitatively the correlation of displacement vector fields (DVFs) from different deformable image registration (DIR) algorithms to register images from helical computed tomography (HCT), axial computed tomography (ACT) and cone beam computed tomography (CBCT) with motion parameters.Materials and methods:CT images obtained from scanning of the mobile phantom were registered with the stationary CT images using four DIR algorithms from the DIRART software: Demons, Fast-Demons, Horn–Schunck and Lucas–Kanade. HCT, ACT and CBCT imaging techniques were used to image a mobile phantom, which included three targets with different sizes (small, medium and large) that were manufactured from a water-equivalent material and embedded in low-density foam to simulate lung lesions. The phantom was moved with controlled cyclic motion patterns where a range of motion amplitudes (0–20 mm) and frequencies (0·125–0·5 Hz) were used.Results:The DVF obtained from different algorithms correlated well with motion amplitudes applied on the mobile phantom for CBCT and HCT, where the maximal DVF increased linearly with the motion amplitudes of the mobile phantom. In ACT, the DVF correlated less with motion amplitudes where motion-induced strong image artefacts and the DIR algorithms were not able to deform the ACT image of the mobile targets to the stationary targets. Three DIR algorithms produce comparable values and patterns of the DVF for certain CT imaging modality. However, DVF from Fast-Demons deviated strongly from other algorithms at large motion amplitudes.Conclusions:The local DVFs provide direct quantitative values for the actual internal tumour shifts that can be used to determine margins for the internal target volume that consider tumour motion during treatment planning. Furthermore, the DVF distributions can be used to extract motion parameters such as motion amplitude that can be extracted from the maximal or minimal DVF calculated by the different DIR algorithms and used in the management of the patient motion.

scholarly journals Intelligent Algorithm-Based Computed Tomography Image Features in Diagnosis of the Effect of Radiofrequency Ablation in Liver Cancer

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yufeng Cha ◽  
Zhili Wei ◽  
Chi Ma ◽  
Lei Zhang
Keyword(s):  
Computed Tomography ◽  
Radiofrequency Ablation ◽  
Ct Images ◽  
Segmentation Algorithm ◽  
Control Group ◽  
Observation Group ◽  
Ct Image ◽  
Comprehensive Diagnosis ◽  
Kappa Value ◽  
The Difference

To provide a reference for finding a reasonable evaluation method for treatment effect of radiofrequency ablation (RFA), computed tomography (CT) image optimized by the intelligent segmentation algorithm was utilized to evaluate the liver condition of hepatocellular carcinoma (HCC) patients after RFA and to estimate the patient’s prognosis. Eighty-eight patients with HCC who needed RFA surgery after diagnosis in our hospital were selected. The CT images before optimization were set as the control group; the CT images after optimization were set as the observation group. Comprehensive diagnosis was taken as the gold standard to compare the ablation range and residual lesions under CT scans before and after surgery. The results showed that the consistency of the two sets of CT images was compared with comprehensive diagnosis under different diameters of the lesion. The difference between the two groups was not statistically considerable when the diameter of the lesion was less than 50 mm ( P > 0.05 ). For lesions larger than 50 mm in diameter, the consistency of the observation group (83%) was remarkably higher than that of the control group (40%), and the difference was substantial ( P < 0.05 ). The kappa value of the observation group was 0.84 and that of the control group was 0.78. The kappa value of observation group was better than the control group, with considerable difference ( P < 0.05 ). In conclusion, the diagnostic effect of CT image based on intelligent segmentation algorithm was superior to conventional diagnosis when the diameter of the lesion was larger than 50 mm. Moreover, the overall improvement rate of patients after RFA treatment was far greater than the recurrence rate, indicating that the clinical adoption of RFA was very meaningful.

scholarly journals Computed Tomography Imaging Findings of Pulmonary Chondroma

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Hexiang Wang ◽  
Pei Nie ◽  
Cheng Dong ◽  
Feng Hou ◽  
Peng Zhang ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Left Lung ◽  
Diagnostic Technique ◽  
Lesion Size ◽  
Ct Images ◽  
Ct Imaging ◽  
Imaging Findings ◽  
Contrast Enhanced Ct ◽  
Pulmonary Chondroma ◽  
The Right

Purpose. To characterize the computed tomography (CT) imaging findings in patients with pulmonary chondroma. Methods. We examined CT imaging findings of eight patients with histopathologically verified pulmonary chondroma. We assessed the location, size, shape, margins, amount of calcification, calcification pattern, and attenuation on precontrast and enhancement CT. Results. All patients exhibited solitary, mildly lobulated pulmonary masses, which were located in the right lung in four cases and the left lung in four cases. The mean lesion size was 3.7 cm (range 0.9–10.7 cm). All eight tumours had a well-defined margin. On plain CT images, seven of the cases (87.5%) showed a mass with varying degrees of calcification, which included strip-like punctate (n=5) and ring (n=2) patterns. One patient with a large lesion (10.7 cm) showed chest wall adhesion. On contrast-enhanced CT images, all lesions demonstrated slight inhomogeneous enhancement ≤14 HU. Conclusion. CT is the reference standard diagnostic technique for locating pulmonary chondroma. In most cases, CT findings show some characteristics that are important in the diagnosis, surgical planning, and follow-up of the tumour.

scholarly journals QUANTIFICATION OF PLEURAL EFFUSION ON CT IMAGES BY AUTOMATIC AND MANUAL SEGMENTATION

2020 ◽  
Vol 6 (5) ◽  
pp. 95-100
Author(s):  
Murk Rehman ◽  
Pertab Rai
Keyword(s):  
Image Processing ◽  
Pleural Effusion ◽  
Digital Image Processing ◽  
Digital Image ◽  
Automatic Segmentation ◽  
Ct Images ◽  
Ct Imaging ◽  
Pleural Disease ◽  
Manual Segmentation ◽  
Reliable Estimation

The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithms. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in the diagnosis of the pleural disease. Pleural effusion is the collection of excess fluid in the pleural cavity. Excessive amount of fluid can impair breathing by limiting the expansion of lungs. Heart failure, cancer, cirrhosis, pneumonia, tuberculosis and many other are the causes of pleural effusion. A number of noninvasive imaging techniques such as radiography, ultrasound and computed tomography (CT) can detect the pleural effusion. The problem faced is the quantification of pleural effusion volume for the purpose of diagnosis of the pleural disease. The objective of this research is to make reliable estimation of pleural effusion volume in CT imaging using digital image processing algorithm. In order to make reliable estimation we need to do the manual and automatic segmentation of CT images and to perform the comparison of automatic and manual segmentation for the quantification of pleural effusion on CT images which provides help in diagnosis of the pleural disease. The results obtained by both the aforementioned techniques indicate that the manual segmentation is better because automated technique has less number of pixels.

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