Self-Supervised Rigid Registration for Multimodal Retinal Images

An, Cheolhong; Wang, Yiqian; Zhang, Junkang; Nguyen, Truong Q.

doi:10.1109/tip.2022.3201476

Cited by 10 publications

(3 citation statements)

References 57 publications

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“…More recently, several works [ 49 – 52 ] have tested deep learning approaches. Some methods leverage the capabilities of deep learning to improve the classical pipelines.…”

Section: Related Workmentioning

confidence: 99%

“…This network is joined with SuperGlue [ 53 ], a graph-neural network capable of point matching, to create an end-to-end training with losses dedicated to the keypoints, the descriptors and their matching. An et al [ 52 ] proposed to refine SuperPoint [ 26 ], a state-of-the-art keypoint detector and descriptor network in natural images. To achieve this, they use the vessel segmentation of multi-modal images.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation

Rivas-Villar¹,

Motschi²,

Pircher³

et al. 2023

Biomed. Opt. Express

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Optical coherence tomography (OCT) is the most widely used imaging modality in ophthalmology. There are multiple variations of OCT imaging capable of producing complementary information. Thus, registering these complementary volumes is desirable in order to combine their information. In this work, we propose a novel automated pipeline to register OCT images produced by different devices. This pipeline is based on two steps: a multi-modal 2D en-face registration based on deep learning, and a Z-axis (axial axis) registration based on the retinal layer segmentation. We evaluate our method using data from a Heidelberg Spectralis and an experimental PS-OCT device. The empirical results demonstrated high-quality registrations, with mean errors of approximately 46 µm for the 2D registration and 9.59 µm for the Z-axis registration. These registrations may help in multiple clinical applications such as the validation of layer segmentations among others.

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“…More recently, several works [ 49 – 52 ] have tested deep learning approaches. Some methods leverage the capabilities of deep learning to improve the classical pipelines.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation

Rivas-Villar¹,

Motschi²,

Pircher³

et al. 2023

Biomed. Opt. Express

View full text Add to dashboard Cite

show abstract

“…A key component of ophthalmology, multimodal retinal image registration, was discussed by the researchers in [3]. They put forth a self-supervised technique for automatically registering fluorescein angiography and color fundus pictures with infrared reflectance.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Jagadale Sachin Mohan

2023

IJRITCC

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The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care.

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A review of deep learning and Generative Adversarial Networks applications in medical image analysis

Sindhura,

Pai,

Bhat

et al. 2024

Multimedia Systems

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Nowadays, computer-aided decision support systems (CADs) for the analysis of images have been a perennial technique in the medical imaging field. In CADs, deep learning algorithms are widely used to perform tasks like classification, identification of patterns, detection, etc. Deep learning models learn feature representations from images rather than handcrafted features. Hence, deep learning models are quickly becoming the state-of-the-art method to achieve good performances in different computer-aided decision-support systems in medical applications. Similarly, deep learning-based generative models called Generative Adversarial Networks (GANs) have recently been developed as a novel method to produce realistic-looking synthetic data. GANs are used in different domains, including medical imaging generation. The common problems, like class imbalance and a small dataset, in healthcare are well addressed by GANs, and it is a leading area of research. Segmentation, reconstruction, detection, denoising, registration, etc. are the important applications of GANs. So in this work, the successes of deep learning methods in segmentation, classification, cell structure and fracture detection, computer-aided identification, and GANs in synthetic medical image generation, segmentation, reconstruction, detection, denoising, and registration in recent times are reviewed. Lately, the review article concludes by raising research directions for DL models and GANs in medical applications.

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Self-Supervised Rigid Registration for Multimodal Retinal Images

Cited by 10 publications

References 57 publications

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation

Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

A review of deep learning and Generative Adversarial Networks applications in medical image analysis

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