Retinal Blood Vessel Segmentation Using Pix2Pix GAN

Popescu, Dan; Deaconu, Mihaela; Ichim, Loretta; Stâmâtescu, Grigore

doi:10.1109/med51440.2021.9480169

Cited by 24 publications

(7 citation statements)

References 9 publications

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“…However, instead of processing the entire picture at once, it classifies portions of the input image as real or fake. The usually used patch size is 70 by 70 pixels, and the final classification results are obtained by averaging the classifications acquired for each patch [45].…”

Section: Cycleganmentioning

confidence: 99%

Enhancing electrical resistance tomography reconstruction from conductive fabric: a comparative study of CycleGAN and pix2pixGAN

Solano Sanchez,

Khambampati,

Jeon

et al. 2024

Meas. Sci. Technol.

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Monitoring fetal health is crucial in prenatal care, and existing techniques for assessing fetal movements are often expensive and limited to clinical environments. This research investigates the potential of utilizing Electrical Resistance Tomography (ERT) with a conductive fabric to create a cost-effective and non-invasive imaging solution for fetal monitoring. The fusion of ERT with wearable e-textile devices facilitates continuous and portable monitoring. To improve the quality of ERT-generated images, we propose the application of CycleGAN and pix2pixGAN, both machine learning models based on generative adversarial networks (GANs). These models learn to map reconstructed images to target images, thereby enhancing reconstruction precision and image quality. The outcomes of this research highlight the effectiveness of the suggested method in managing noisy data and achieving superior image generation. This work presents a promising approach to fetal monitoring using ERT and deep learning techniques, opening possibilities for more affordable and accessible prenatal care.

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Section: Cycleganmentioning

confidence: 99%

Enhancing electrical resistance tomography reconstruction from conductive fabric: a comparative study of CycleGAN and pix2pixGAN

Solano Sanchez,

Khambampati,

Jeon

et al. 2024

Meas. Sci. Technol.

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“…Before diffusion models became popular in medical image analysis or in mainstream computer vision, GANs [22] were the most popular image generation methods. Developed to perform conditional natural image generation, Pix2PixGAN [23] was adapted to medical imaging and several researchers have shown its usefulness in such tasks [24][25][26][27]. Zhu et al [28] proposed CycleGAN to perform conditional image-to-image translation between two domains using unpaired images, and the model has also been extensively used in medical imaging.…”

Section: Related Workmentioning

confidence: 99%

Med-cDiff: Conditional Medical Image Generation with Diffusion Models

Hung,

Zhao,

Zheng

et al. 2023

Bioengineering

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Conditional image generation plays a vital role in medical image analysis as it is effective in tasks such as super-resolution, denoising, and inpainting, among others. Diffusion models have been shown to perform at a state-of-the-art level in natural image generation, but they have not been thoroughly studied in medical image generation with specific conditions. Moreover, current medical image generation models have their own problems, limiting their usage in various medical image generation tasks. In this paper, we introduce the use of conditional Denoising Diffusion Probabilistic Models (cDDPMs) for medical image generation, which achieve state-of-the-art performance on several medical image generation tasks.

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“…In the same context, some authors used a Generative Adversarial Network (GAN) to segment retinal vasculature. For example, the authors in [33] proposed a conditional pix2pix GAN for segmenting retinal vessels, while in [34] the authors proposed a GAN-based model with an adapted UNet to segment retinal data. In [35], the authors proposed a GAN-based model named M-GAN with an M-generator while two encoder-decoder networks were exploited.…”

Section: Diabetic Retinopathy Retinal Vasculature Segmentationmentioning

confidence: 99%

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

Sebastian,

Elharrouss,

Al-Maadeed

et al. 2023

Bioengineering

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Most diabetes patients develop a condition known as diabetic retinopathy after having diabetes for a prolonged period. Due to this ailment, damaged blood vessels may occur behind the retina, which can even progress to a stage of losing vision. Hence, doctors advise diabetes patients to screen their retinas regularly. Examining the fundus for this requires a long time and there are few ophthalmologists available to check the ever-increasing number of diabetes patients. To address this issue, several computer-aided automated systems are being developed with the help of many techniques like deep learning. Extracting the retinal vasculature is a significant step that aids in developing such systems. This paper presents a GAN-based model to perform retinal vasculature segmentation. The model achieves good results on the ARIA, DRIVE, and HRF datasets.

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Retinal Blood Vessel Segmentation Using Pix2Pix GAN

Cited by 24 publications

References 9 publications

Enhancing electrical resistance tomography reconstruction from conductive fabric: a comparative study of CycleGAN and pix2pixGAN

Enhancing electrical resistance tomography reconstruction from conductive fabric: a comparative study of CycleGAN and pix2pixGAN

Med-cDiff: Conditional Medical Image Generation with Diffusion Models

GAN-Based Approach for Diabetic Retinopathy Retinal Vasculature Segmentation

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