Impact of Enhancement for Coronary Artery Segmentation Based on Deep Learning Neural Network

Blaiech, Ahmed Ghazi; Mansour, A. Ben; Kerkeni, Asma; Bedoui, Mohamed Hédi; Abdallah, Asma Ben

doi:10.1007/978-3-030-31321-0_23

Cited by 12 publications

(6 citation statements)

References 25 publications

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“…To solve the above-mentioned architectural problems, many U-Net-based network deformation structures have been proposed and achieved good results. A new U-Net-based DNN structure was proposed to improve the segmentation effect by Ahmed et al [14] This method selected various enhancement methods (e.g. histogram and Frangi multi-scale ltering) according to the background to remove noise and improve segment effect.…”

Section: Related Workmentioning

confidence: 99%

DBCU-Net: deep learning approach for segmentation of coronary angiography images

Shen

Chen

Tong

et al. 2023

Int J Cardiovasc Imaging

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Coronary angiography (CAG) is the "gold standard" for diagnosing coronary artery disease (CAD).However, due to the limitation of current imaging methods, the CAG image has low resolution and poor contrast with a lot of artifacts and noise, which makes it di cult for blood vessels segmentation. In this paper, we propose a DBCU-Net for automatic segmentation of CAG images, which is an extension of U-Net, DenseNet with bidirectional convLSTM. The main contribution of our network is that instead of convolution in the feature extraction of U-Net, we incorporate dense connectivity and the bidirectional convLSTM to highlight salient features. We conduct our experiment on our private dataset, and achieve average Accuracy, Precision, Recall and F1-score for coronary artery segmentation of 0.985, 0.913, 0.847 and 0.879 respectively. IntroductionCoronary artery disease (CAD) is a common heart disease, which is the leading cause of death worldwide according to the World Health Organization (WHO) [1]. Globally, the number of patients with CAD is expected to increase from 327.9 million in 2017 to 365.9 million in 2026. CAD poses a huge threat to human life. An accurate diagnosis of CAD is particularly important. With the development of computer diagnosis and treatment technologies, more powerful medical methods, such as magnetic resonance imaging(MRI), computed tomography(CT) [2], and X-ray coronary angiography(CAG) [3], are proposed to assist diagnosis. As the "gold standard" for the diagnosis of CAD, CAG can precisely pinpoint the site and the degree of coronary artery stenosis, as well as the symptoms of the condition.Accurate medical image analysis is crucial to subsequent clinical diagnosis and treatment. Diagnosis and treatment of clinical diseases mainly rely on advanced instruments and doctors' high-precision technology. However, manual segmentation of such CAG images requires a lot of medical expertise,

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Section: Related Workmentioning

confidence: 99%

DBCU-Net: deep learning approach for segmentation of coronary angiography images

Shen

Chen

Tong

et al. 2023

Int J Cardiovasc Imaging

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“…In Soomro et al [8], morphological operators and the contrast limited adaptive histogram equalization (CLAHE) technique were used to enhance and segment the retinal vessels. In Blaiech et al [9], to study the effect of enhancement, CLAHE, Frangi and ranking the orientation response of path operators(RORPO) were used to enhance the coronary artery for the purpose of segmentation.…”

Section: Related Workmentioning

confidence: 99%

“…Though deep learning-based segmentation is proven to be efficient, the way of data handling and the enhancement of images that go into the deep learning model has a major influence on the precise segmentation. Especially for complex structures such as vessels, enhancement techniques are proven to be effective prior to segmentation and visualization [7][8][9]. In particular, Hessian-based vessel enhancement filters are most popularly used compared to other techniques [10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation

et al. 2021

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Colorectal cancer (CRC) is the third most common type of cancer with the liver being the most common site for cancer spread. A precise understanding of patient liver anatomy and pathology, as well as surgical planning based on that, plays a critical role in the treatment process. In some cases, surgeons request a 3D reconstruction, which requires a thorough analysis of the available images to be converted into 3D models of relevant objects through a segmentation process. Liver vessel segmentation is challenging due to the large variations in size and directions of the vessel structures as well as difficult contrasting conditions. In recent years, deep learning-based methods had been outperforming the conventional image analysis methods in the field of medical imaging. Though Convolutional Neural Networks (CNN) have been proved to be efficient for the task of medical image segmentation, the way of handling the image data and the preprocessing techniques play an important role in segmentation. Our work focuses on the combination of different vesselness enhancement filters and preprocessing methods to enhance the hepatic vessels prior to segmentation. In the first experiment, the effect of enhancement using individual vesselness filters was studied. In the second experiment, the effect of gamma correction on vesselness filters was studied. Lastly, the effect of fused vesselness filters over individual filters was studied. The methods were evaluated on clinical CT data. The quantitative analysis of the results in terms of different evaluation metrics from experiments can be summed up as (i) each of the filtered methods shows an improvement as compared to unenhanced with the best mean DICE score of 0.800 in comparison to 0.740 for unenhanced; (ii) applied gamma correction provides a statistically significant improvement in the performance of each filter with improvement in mean DICE of around 2%; (iii) both the fused filtered images and fused segmentation give the best results (mean DICE score of 0.818 and 0.830, respectively) with the statistically significant improvement compared to the individual filters with and without Gamma correction. The results have further been verified by qualitative analysis and hence show the importance of our proposed fused filter and segmentation approaches.

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“…The segmentation results achieve the accuracy needed in practical clinical applications. [23]. These methods utilized to directly enhance the target tissues are not applicable to the cancerous esophagus since it has various anatomical structures.…”

Section: )mentioning

confidence: 99%

Eso-Net: A Novel 2.5D Segmentation Network With the Multi-Structure Response Filter for the Cancerous Esophagus

Zhou

Huang

et al. 2020

IEEE Access

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Automatic segmentation of the cancerous esophagus in computed tomography (CT) images is a computer-assisted method that can improve the efficiency of the diagnosis and treatment. Due to the diversity of the cancer stage and location, the anatomical structure of the cancerous esophagus is various. Moreover, the low contrast against surrounding tissues leads to a blurry boundary of the cancerous esophagus. Therefore, existing segmentation networks cannot achieve satisfactory results in automatic segmentation of the cancerous esophagus. In this paper, we propose a novel 2.5D segmentation network named Eso-Net for the cancerous esophagus based on an encoder-decoder architecture. A 3D enhancement filter called Multi-Structure Response Filter (MSRF) is designed to extract 3D structural information as prior knowledge. Furthermore, dilated convolutions and residual connections are employed in the convolutional blocks of Eso-Net for multi-scale feature learning. With 3D structural priors, Prior Attention Modules (PAM) are incorporated into the network to facilitate the transmission of relevant spatial information. The experiments are conducted on the dataset from 30 esophageal cancer patients, and we report an 84.839% dice similarity coefficient, an 85.955% precision, an 83.752% sensitivity, and a 2.583mm Hausdorff distance. The experimental results demonstrate that the proposed method outperforms other existing segmentation networks in this task and can effectively assist doctors in the diagnosis and treatment of esophageal cancer.

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Impact of Enhancement for Coronary Artery Segmentation Based on Deep Learning Neural Network

Cited by 12 publications

References 25 publications

DBCU-Net: deep learning approach for segmentation of coronary angiography images

DBCU-Net: deep learning approach for segmentation of coronary angiography images

Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation

Eso-Net: A Novel 2.5D Segmentation Network With the Multi-Structure Response Filter for the Cancerous Esophagus

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