296 research outputs found
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age
adults. DR lesions can be challenging to identify in fundus images, and
automatic DR detection systems can offer strong clinical value. Of the publicly
available labeled datasets for DR, the Indian Diabetic Retinopathy Image
Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of
four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard
exudates. We utilize the HEDNet edge detector to solve a semantic segmentation
task on this dataset, and then propose an end-to-end system for pixel-level
segmentation of DR lesions by incorporating HEDNet into a Conditional
Generative Adversarial Network (cGAN). We design a loss function that adds
adversarial loss to segmentation loss. Our experiments show that the addition
of the adversarial loss improves the lesion segmentation performance over the
baseline.Comment: Accepted to International Conference on Image Analysis and
Recognition, ICIAR 2019. Published at
https://doi.org/10.1007/978-3-030-27272-2_29 Code:
https://github.com/zoujx96/DR-segmentatio
Deep learning for diabetic retinopathy detection and classification based on fundus images: A review.
Diabetic Retinopathy is a retina disease caused by diabetes mellitus and it is the leading cause of blindness globally. Early detection and treatment are necessary in order to delay or avoid vision deterioration and vision loss. To that end, many artificial-intelligence-powered methods have been proposed by the research community for the detection and classification of diabetic retinopathy on fundus retina images. This review article provides a thorough analysis of the use of deep learning methods at the various steps of the diabetic retinopathy detection pipeline based on fundus images. We discuss several aspects of that pipeline, ranging from the datasets that are widely used by the research community, the preprocessing techniques employed and how these accelerate and improve the models' performance, to the development of such deep learning models for the diagnosis and grading of the disease as well as the localization of the disease's lesions. We also discuss certain models that have been applied in real clinical settings. Finally, we conclude with some important insights and provide future research directions
Generative adversarial networks in ophthalmology: what are these and how can they be used?
PURPOSE OF REVIEW: The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images. RECENT FINDINGS: Image synthesis is the most relevant function of GANs to the medical field, and it has been widely used for generating 'new' medical images of various modalities. In ophthalmology, GANs have mainly been utilized for augmenting classification and predictive tasks, by synthesizing fundus images and optical coherence tomography images with and without pathologies such as age-related macular degeneration and diabetic retinopathy. Despite their ability to generate high-resolution images, the development of GANs remains data intensive, and there is a lack of consensus on how best to evaluate the outputs produced by GANs. SUMMARY: Although the problem of artificial biomedical data generation is of great interest, image synthesis by GANs represents an innovation with yet unclear relevance for ophthalmology
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due
to the advantage of not requiring pupillary dilation, however, is prone to poor
quality due to operators, systemic imperfections, or patient-related causes.
Optimal retinal image quality is mandated for accurate medical diagnoses and
automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to
propose an unpaired image-to-image translation scheme for mapping low-quality
retinal CFPs to high-quality counterparts. Furthermore, to improve the
flexibility, robustness, and applicability of our image enhancement pipeline in
the clinical practice, we generalized a state-of-the-art model-based image
reconstruction method, regularization by denoising, by plugging in priors
learned by our OT-guided image-to-image translation network. We named it as
regularization by enhancing (RE). We validated the integrated framework, OTRE,
on three publicly available retinal image datasets by assessing the quality
after enhancement and their performance on various downstream tasks, including
diabetic retinopathy grading, vessel segmentation, and diabetic lesion
segmentation. The experimental results demonstrated the superiority of our
proposed framework over some state-of-the-art unsupervised competitors and a
state-of-the-art supervised method.Comment: Accepted as a conference paper to The 28th biennial international
conference on Information Processing in Medical Imaging (IPMI 2023
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