Three-dimensional reconstructions of carotid bifurcation from CT images: evaluation of different rendering methods

Abstract Introduction: Rotational deformities following IM nailing of tibia has a reported incidence of as high as 20%. Common techniques to measure deformities following IM nailing of tibia are either based on clinical assessment, plain X-rays or CT-scan comparing the treated leg with the uninjured contralateral side. All these techniques are based on examiners manual calculation inherently subject to bias. Following our previous rigorous motion analysis and symmetry studies on hemi pelvises, femurs and orthopaedic implants, we aimed to introduce a novel fully digital technique to measure rotational deformities in the lower legs.Material and Methods: Following formal institutional approval from the Imperial College, CT images of 10 pairs of human lower legs were retrieved. Images were anonymized and uploaded to a research server. Three dimensional CT images of the lower legs were bilaterally reconstructed. The mirrored images of the left side were merged with the right side proximally as stationary and distally as moving objects. Discrepancies in translation and rotation were automatically calculated.Results: Our study population had a mean age of 54 ± 20 years. There were six males and four females. We observed a greater variation in translation (mm) of Centre of Mass (COM) in sagittal plane (CI: -2.959--.292) which was also presented as rotational difference alongside the antero-posterior direction or Y axis (CI: .370-1.035). In other word the right lower legs in our study were more likely to be in varus compared to the left side. However, there were no statistically significant differences in coronal or axial planes.Conclusion: Using our proposed fully digital technique we found that lower legs of the human adults were symmetrical in axial and coronal plane. We found sagittal plane differences which need further addressing in future using bigger sample size. Our novel recommended technique is fully digital and commercially available. This new technique can be useful in clinical practice addressing rotational deformities following orthopaedic surgical intervention. This new technique can substitute the previously introduced techniques.

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AUTOMATIC LUNG NODULE DETECTION BASED ON STATISTICAL REGION MERGING AND SUPPORT VECTOR MACHINES

Image Analysis & Stereology ◽

10.5566/ias.1679 ◽

2017 ◽

Vol 36 (2) ◽

pp. 65 ◽

Cited By ~ 7

Author(s):

Elaheh Aghabalaei Khordehchi ◽

Ahmad Ayatollahi ◽

Mohammad Reza Daliri

Keyword(s):

Lung Nodule ◽

Three Dimensional ◽

Pulmonary Nodules ◽

Ct Images ◽

Support Vector ◽

Svm Classifier ◽

Region Merging ◽

Three Dimensional Objects ◽

Ct Data ◽

Statistical Region Merging

Lung cancer is one of the most common diseases in the world that can be treated if the lung nodules are detected in their early stages of growth. This study develops a new framework for computer-aided detection of pulmonary nodules thorough a fully-automatic analysis of Computed Tomography (CT) images. In the present work, the multi-layer CT data is fed into a pre-processing step that exploits an adaptive diffusion-based smoothing algorithm in which the parameters are automatically tuned using an adaptation technique. After multiple levels of morphological filtering, the Regions of Interest (ROIs) are extracted from the smoothed images. The Statistical Region Merging (SRM) algorithm is applied to the ROIs in order to segment each layer of the CT data. Extracted segments in consecutive layers are then analyzed in such a way that if they intersect at more than a predefined number of pixels, they are labeled with a similar index. The boundaries of the segments in adjacent layers which have the same indices are then connected together to form three-dimensional objects as the nodule candidates. After extracting four spectral, one morphological, and one textural feature from all candidates, they are finally classified into nodules and non-nodules using the Support Vector Machine (SVM) classifier. The proposed framework has been applied to two sets of lung CT images and its performance has been compared to that of nine other competing state-of-the-art methods. The considerable efficiency of the proposed approach has been proved quantitatively and validated by clinical experts as well.

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Pulmonary Lesion Subtypes Recognition of COVID-19 From Radiomics Data With Three Dimensional Texture Characterization in CT Images

10.21203/rs.3.rs-532131/v1 ◽

2021 ◽

Author(s):

Wei Li ◽

Yangyong Cao ◽

Kun Yu ◽

Yibo Cai ◽

Feng Huang ◽

...

Keyword(s):

Data Augmentation ◽

Critical Role ◽

Three Dimensional ◽

Treatment Decision ◽

Ground Glass Opacity ◽

Ct Images ◽

Classification Model ◽

Volume Data ◽

Selection Algorithm ◽

Adaptive Selection

Abstract Background: The COVID-19 disease is putting unprecedented pressure on the global healthcare system. The CT examination as a auxiliary confirmed diagnostic method can help clinicians quickly detect lesions locations of COVID-19 once screening by PCR test. Furthermore, the lesion subtypes classification plays a critical role in the consequent treatment decision. Identifying the subtypes of lesions accurately can help doctors discover changes in lesions in time and better assess the severity of COVID-19. Method: The most four typical lesion subtypes of COVID-19 are discussed in this paper, which are ground-glass opacity (GGO), cord, solid and subsolid. A computer aided diagnosis approach of lesion subtype is proposed in this paper. The radiomics data of lesions are segmented from COVID-19 patients CT images with diagnosis and lesions annotations by radiologists. Then the three dimensional texture descriptors are applied on the volume data of lesions as well as shape and First order features. The massive feature data are selected by hybrid adaptive selection algorithm and a classification model is trained at the same time. The classifier is used to predict lesion subtypes as side decision information for radiologists. Results: There are 3734 lesions extracted from the dataset with 319 patients collection and then 189 radiomics features are obtained finally. The random forest classifier is trained with data augmentation that the number of different subtypes of lesions is imbalanced in initial dataset. The experimental results show that the accuracy of the four subtypes of lesions is (0.9306, 0.9684, 0.9958, and 0.9430), the recall is (0.9552, 0.9158, 0.9580 and 0.8075) and the f-score is (0.93.84, 0.92.37, 0.95.47, and 84.42). Conclusion: The method is evaluated in multiple sufficient experiments. The results show that the 3D radiomics features chosen by hybrid adaptive selection algorithm can better express the advanced information of the lesion data. The classification model obtains a good performance and is compared the models of COVID-19 in the stat of art, which can help clinicians more accurately identify the subtypes of COVID-19 lesions and provide help for further research.

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Three-dimensional segmentation of CT images using neural network

Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Vol.20 Biomedical Engineering Towards the Year 2000 and Beyond (Cat. No.98CH36286) ◽

10.1109/iembs.1998.745471 ◽

2002 ◽

Author(s):

Xudong Bao ◽

Shaojun Xiao ◽

Zhengquan Xu

Keyword(s):

Neural Network ◽

Three Dimensional ◽

Ct Images ◽

Three Dimensional Segmentation

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Investigation of three-dimensional printing materials for printing aorta model replicating Type B aortic dissection

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405617666210218102046 ◽

2021 ◽

Vol 17 ◽

Author(s):

Chia-An Wu ◽

Andrew Squelch ◽

Zhonghua Sun

Keyword(s):

3D Printing ◽

Aortic Dissection ◽

Three Dimensional ◽

Ct Images ◽

Patient Specific ◽

Type B ◽

Type B Aortic Dissection ◽

Ct Attenuation ◽

Significant Difference ◽

3D Printed

Aim: To determine a printing material that has both elastic property and radiology equivalence close to real aorta for simulation of endovascular stent graft repair of aortic dissection. Background: With the rapid development of three-dimensional (3D) printing technology, a patient-specific 3D printed model is able to help surgeons to make better treatment plan for Type B aortic dissection patients. However, the radiological properties of most 3D printing materials have not been well characterized. This study aims to investigate the appropriate materials for printing human aorta with mechanical and radiological properties similar to the real aortic computed tomography (CT) attenuation. Objective: Quantitative assessment of CT attenuation of different materials used in 3D printed models of aortic dissection for developing patient-specific 3D printed aorta models to simulate type B aortic dissection. Method: A 25-mm length of aorta model was segmented from a patient’s image dataset with diagnosis of type B aortic dissection. Four different elastic commercial 3D printing materials, namely Agilus A40 and A50, Visijet CE-NT A30 and A70 were selected and printed with different hardness. Totally four models were printed out and conducted CT scanned twice on a 192-slice CT scanner using the standard aortic CT angiography protocol, with and without contrast inside the lumen.Five reference points with region of interest (ROI) of 1.77 mm2 were selected at the aortic wall and intimal flap and their Hounsfield units (HU) were measured and compared with the CT attenuation of original CT images. The comparison between the patient’s aorta and models was performed through a paired-sample t-test to determine if there is any significant difference. Result: The mean CT attenuation of aortic wall of the original CT images was 80.7 HU. Analysis of images without using contrast medium showed that the material of Agilus A50 produced the mean CT attenuation of 82.6 HU, which is similar to that of original CT images. The CT attenuation measured at images acquired with other three materials was significantly lower than that of original images (p<0.05). After adding contrast medium, Visijet CE-NT A30 had an average CT attenuation of 90.6 HU, which is close to that of the original images with statistically significant difference (p>0.05). In contrast, the CT attenuation measured at images acquired with other three materials (Agilus A40, A50 and Visiject CE-NT A70) was 129 HU, 135 HU and 129.6 HU, respectively, which is significantly higher than that of original CT images (p<0.05). Conclusion: Both Visijet CE-NT and Agilus have tensile strength and elongation close to real patient’s tissue properties producing similar CT attenuation. Visijet CE-NT A30 is considered the appropriate material for printing aorta to simulate contrast-enhanced CT imaging of type B aortic dissection. Due to lack of body phantom in the experiments, further research with simulation of realistic anatomical body environment should be conducted.

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