Contrast agent-free synthesis and segmentation of ischemic heart disease images using progressive sequential causal GANs

Xu, Chenchu; Xu, Lei; Ohorodnyk, Pavlo; Roth, M.W.; Chen, Bin; Li, Shuo

doi:10.1016/j.media.2020.101668

Cited by 61 publications

(45 citation statements)

References 19 publications

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“…Chlebus [52] used UNet with random forest to get the DSC with 65 and Han [37] used Res-UNet to get the DSC with 67. Three popular networks (FCN, UNet, ResNets) and two cutting edge representative segmentation methods using GANs ( SynSeg-Net [42], and PSCGANs [45]) are tested using our data sets (non-contrast image, T2FS and contrast-enhanced image, delay-DE images) verify the validity of the models. The results are shown in Tab.…”

Section: Comparison With State-of-art Modelmentioning

confidence: 91%

“…Huo et al [42] proposed a SynSeg-Net to perform segmentation tasks on the medical image without ground truth labels [Reference]. And Xu et al [45] proposed a PSCGAN that simultaneously synthesizes an equivalent image of late-gadolinium-enhancement and segment the diagnosis-related tissues from cine MR images [Reference]. All these works demonstrated that GAN has great power in the field of medical image analysis.…”

Section: Convolutionalmentioning

confidence: 99%

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Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

et al. 2021

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Section: Comparison With State-of-art Modelmentioning

confidence: 91%

Section: Convolutionalmentioning

confidence: 99%

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

et al. 2021

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“…(DRGAN) GANs in Generation Medical Images: Medical image synthesis with GANs [28] may help to solve the problem of a scarcity of big, varied annotated datasets. Methods which are suggested for a number of imaging domains in medical field, e.g, magnetic resonance imaging (MRI) [29][30][31], chest X-rays [32] and computed tomography (CT), [33][34][35]. Radiation exposure through CT imaging, for example, increases the risk of cancer.…”

Section: Related Workmentioning

confidence: 99%

DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network

et al. 2022

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— Diabetic Retinopathy (DR) is a serious consequence of diabetes that seriously impact on the eyes and is a leading cause of blindness. If the lesions in DR arise in the central portion of the fundus, they may result in significant vision loss, which we refer to as Diabetic Macular Edema (DME). Deep learning (DL) techniques are commonly used utilized in ophthalmology for discriminative tasks such as diabetic retinopathy or age-related macular degeneration (AMD) diagnosis. Deep learning techniques typically need huge picture data sets for deep convolutional neural networks (DCNNs) training, it should be graded by human specialists. According to international protocol, it is classified into five severity categories. However, improving a grading model for high generality needs a significant quantity of balanced training data, which is challenging to obtain, especially at high levels of severity. Typical techniques for data augmentation, in many applications of deep learning in the retinal image processing domain, the difficulty of access to huge annotated datasets and legal concerns about patient privacy are limiting issues. As a result, the concept of creating synthetic retinal pictures that are indistinguishable from actual data has garnered more attention. GANs have been certain to be an effective framework for creating synthetic databases of anatomically accurate retinal fundus pictures. GANs, in particular, have garnered increasing attention in ophthalmology. in this article, we present a loss-less generative adversarial network (DR-LL GAN) to generate good resolution fundus pictures that May be adjusted to include random grading and information about the lesion. As a result, large-scale generated data may be used to train a DR grading and lesion segmentation model with more appropriate augmentation. Our model experiments evaluated on IDRID and MESSIDOR datasets, it's obtained a discrimination loss of 0.69374 and a generation loss of 1.10438, as well as a segmentation accuracy of 0.9840 in our tests. This might support in the optimization techniques of the neural network design and in computer-aided screening of medical picture, thus increasing diagnostic reliability for clinical assessment in the future of sophisticated technological healthcare.

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“…Zhang et al proposed a deep learning based framework to greatly improve the efficacy of the segmentation of LV scar on cine MRI (with its stages consisting of (1) ROI localization, (2) RNN based motion pattern extraction, and (3) pixel classification by FCNN) and assess their network extensively under a clinical setting ( Zhang et al, 2019 ). Xu et al (2020) on top of the deep learning based workflow, proposed a progressive sequential causal generative adversarial network (GAN) to simultaneously synthesize LGE-equivalent images and multi-class tissue segmentation (including LV blood cavity, LV myocardium and scar region) from cine CMR images. A detailed summary and results of a private benchmarking of all these algorithms can be found in Table 8 .…”

Section: Scar Segmentation With Non-contrast-agent (Non-ca) Enhanced Imaging Modality Onlymentioning

confidence: 99%

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

Tang

et al. 2021

Front. Physiol.

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Segmentation of cardiac fibrosis and scars is essential for clinical diagnosis and can provide invaluable guidance for the treatment of cardiac diseases. Late Gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) has been successful in guiding the clinical diagnosis and treatment reliably. For LGE CMR, many methods have demonstrated success in accurately segmenting scarring regions. Co-registration with other non-contrast-agent (non-CA) modalities [e.g., balanced steady-state free precession (bSSFP) cine magnetic resonance imaging (MRI)] can further enhance the efficacy of automated segmentation of cardiac anatomies. Many conventional methods have been proposed to provide automated or semi-automated segmentation of scars. With the development of deep learning in recent years, we can also see more advanced methods that are more efficient in providing more accurate segmentations. This paper conducts a state-of-the-art review of conventional and current state-of-the-art approaches utilizing different modalities for accurate cardiac fibrosis and scar segmentation.

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Contrast agent-free synthesis and segmentation of ischemic heart disease images using progressive sequential causal GANs

Cited by 61 publications

References 19 publications

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

Segmentation of Liver Lesions Without Contrast Agents With Radiomics-Guided Densely UNet-Nested GAN

DR-LL Gan: Diabetic Retinopathy Lesions Synthesis using Generative Adversarial Network

Recent Advances in Fibrosis and Scar Segmentation From Cardiac MRI: A State-of-the-Art Review and Future Perspectives

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