Comparison of Denoising Methods Applied to CTA Images of 3D Segmentation of Aortic Dissection

Fitria, Maya; Adriman, Ramzi; Morariu, Cosmin Adrian; Pauli, Josef

doi:10.1109/cyberneticscom.2019.8875635

Cited by 3 publications

(9 citation statements)

References 9 publications

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“…Different from the flat function, using the other kernel functions return blurry images. The visual results are contrary to the results that have been proven in [8] and [9], where the NLM algorithm shows outstanding visual performance. The poor-quality performance of NLM occurs because of resolution range in MPR images extracted by the system is too small, which is in the range of 0 to 1.…”

Section: Resultscontrasting

confidence: 98%

“…This work uses Computed Tomography Angiography (CTA) images of patients with aortic dissection cases provided by West German Heart Center of Essen-University Hospital. The number of datasets used for examination is 11 datasets sliced between 89-1034 slices with a 0.7-5 mm slice gap, and each axial slice has a resolution range between 0.445 to 0.863 mm [9]. The implementation of NLM methods in this experiment adopted the source from [21].…”

Section: A Dataset and Methods Implementationmentioning

confidence: 99%

“…Qualitative assessment is done by analyzing the quality of the output image. On the other hand, the quantitative model uses the measurement of dice similarity coefficient (DSC), precision (P), and recall (R) based on four combinations above by using ( 7)- (9). The DSC will give values between 0 to 1.…”

Section: B Evaluation Modelmentioning

confidence: 99%

“…Hence, removing noise in the MPR image is mandatory to increase aorta localization's whole performance. In [9], some denoising techniques, such as Gaussian filter, anisotropic diffusion, Yaroslavsky filter, and bilateral filter, are implemented in the system. The segmentation results obtained are quite encouraging.…”

Section: Introductionmentioning

confidence: 99%

“…This work aims to continue the work that has been done in [9] by embedding another denoising method, i.e., NLM, to CTA data of aortic dissection. This filter can exploit the inherent redundant information and preserve high degree image texture and details in CT scan images [5].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Implementing a non-local means method to CTA data of aortic dissection

Fitria

Morariu

Pauli

et al. 2021

J. Teknol. dan Sist. Komput

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It is necessary to conserve important information, like edges, details, and textures, in CT aortic dissection images, as this helps the radiologist examine and diagnose the disease. Hence, a less noisy image is required to support medical experts in performing better diagnoses. In this work, the non-local means (NLM) method is conducted to minimize the noise in CT images of aortic dissection patients as a preprocessing step to produce accurate aortic segmentation results. The method is implemented in an existing segmentation system using six different kernel functions, and the evaluation is done by assessing DSC, precision, and recall of segmentation results. Furthermore, the visual quality of denoised images is also taken into account to be determined. Besides, a comparative analysis between NLM and other denoising methods is done in this experiment. The results showed that NLM yields encouraging segmentation results, even though the visualization of denoised images is unacceptable. Applying the NLM algorithm with the flat function provides the highest DSC, precision, and recall values of 0.937101, 0.954835, and 0.920517 consecutively.

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Section: Resultscontrasting

confidence: 98%

Section: A Dataset and Methods Implementationmentioning

confidence: 99%

Section: B Evaluation Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Implementing a non-local means method to CTA data of aortic dissection

Fitria

Morariu

Pauli

et al. 2021

J. Teknol. dan Sist. Komput

Self Cite

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show abstract

Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification

Chen

Zhang

Mei

et al. 2021

Medical Image Analysis

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Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning

Zhang,

Cheng,

Han

et al. 2023

Phys. Med. Biol.

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Objective. Type-b aortic dissection (AD) is a life-threatening cardiovascular disease and the primary treatment is thoracic endovascular aortic repair (TEVAR). Due to the lack of a rapid and accurate segmentation technique, the patient-specific postoperative AD model is unavailable in clinical practice, resulting in impracticable 3-D morphological and hemodynamic analyses during TEVAR assessment. This work aims to construct a deep learning-based segmentation framework for postoperative type-b AD.Approach. The segmentation is performed in a two-stage manner. A multi-class segmentation of the contrast-enhanced aorta, thrombus (TH), and branch vessels (BV) is achieved in the first stage based on the cropped image patches. True lumen (TL) and false lumen (FL) are extracted from a straightened image containing the entire aorta in the second stage. A global-local fusion learning mechanism is designed to improve the segmentation of TH and BR by compensating for the missing contextual features of the cropped images in the first stage.Results. The experiments are conducted on a multi-center dataset comprising 133 patients with 306 follow-up images. Our framework achieves the state-of-the-art dice similarity coefficient (DSC) of 0.962, 0.921, 0.811, and 0.884 for TL, FL, TH, and BV, respectively. The global-local fusion learning mechanism increases the DSC of TH and BV by 2.3% (p<0.05) and 1.4% (p<0.05), respectively, based on the baseline. Segmenting TH in stage 1 can achieve significantly better DSC for FL (0.921±0.055 vs. 0.857±0.220, p<0.01) and TH (0.811±0.137 vs. 0.797±0.146, p<0.05) than in stage 2. Our framework supports more accurate vascular volume quantifications compared with previous segmentation model, especially for the patients with enlarged TH+FL after TEVAR, and shows good generalizability to different hospital settings.Significance. Our framework can quickly provide accurate patient-specific AD models, supporting the clinical practice of 3-D morphological and hemodynamic analyses for quantitative and more comprehensive patient-specific TEVAR assessments.

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Comparison of Denoising Methods Applied to CTA Images of 3D Segmentation of Aortic Dissection

Cited by 3 publications

References 9 publications

Implementing a non-local means method to CTA data of aortic dissection

Implementing a non-local means method to CTA data of aortic dissection

Multi-stage learning for segmentation of aortic dissections using a prior aortic anatomy simplification

Deep learning-based multi-stage postoperative type-b aortic dissection segmentation using global-local fusion learning

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