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LE BOITE H, COUTURIER A, TADAYONI R, LAMARD M, QUELLEC G. VMseg: Using spatial variance to automatically segment retinal non-perfusion on OCT-angiography. PLoS One 2024; 19:e0306794. [PMID: 39110715 PMCID: PMC11305542 DOI: 10.1371/journal.pone.0306794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2024] [Accepted: 06/24/2024] [Indexed: 08/10/2024] Open
Abstract
BACKGROUND AND OBJECTIVES To develop and test VMseg, a new image processing algorithm performing automatic segmentation of retinal non-perfusion in widefield OCT-Angiography images, in order to estimate the non-perfusion index in diabetic patients. METHODS We included diabetic patients with severe non-proliferative or proliferative diabetic retinopathy. We acquired images using the PlexElite 9000 OCT-A device with a photomontage of 5 images of size 12 x 12 mm. We then developed VMseg, a Python algorithm for non-perfusion detection, which binarizes a variance map calculated through convolution and morphological operations. We used 70% of our data set (development set) to fine-tune the algorithm parameters (convolution and morphological parameters, binarization thresholds) and evaluated the algorithm performance on the remaining 30% (test set). The obtained automatic segmentations were compared to a ground truth corresponding to manual segmentation from a retina expert and the inference processing time was estimated. RESULTS We included 51 eyes of 30 patients (27 severe non-proliferative, 24 proliferative diabetic retinopathy). Using the optimal parameters found on the development set to tune the algorithm, the mean dice for the test set was 0.683 (sd = 0.175). We found a higher dice coefficient for images with a higher area of retinal non-perfusion (rs = 0.722, p < 10-4). There was a strong correlation (rs = 0.877, p < 10-4) between VMseg estimated non-perfusion indexes and indexes estimated using the ground truth segmentation. The Bland-Altman plot revealed that 3 eyes (5.9%) were significantly under-segmented by VMseg. CONCLUSION We developed VMseg, an automatic algorithm for retinal non-perfusion segmentation on 12 x 12 mm OCT-A widefield photomontages. This simple algorithm was fast at inference time, segmented images in full-resolution and for the OCT-A format, was accurate enough for automatic estimation of retinal non-perfusion index in diabetic patients with diabetic retinopathy.
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Affiliation(s)
- Hugo LE BOITE
- Université Paris Cité, Paris, France
- Ophthalmology Department, AP-HP, Hôpital Lariboisière, Paris, France
| | - Aude COUTURIER
- Université Paris Cité, Paris, France
- Ophthalmology Department, AP-HP, Hôpital Lariboisière, Paris, France
| | - Ramin TADAYONI
- Université Paris Cité, Paris, France
- Ophthalmology Department, AP-HP, Hôpital Lariboisière, Paris, France
| | - Mathieu LAMARD
- Université de Bretagne Occidentale, Brest, France
- LaTIM, INSERM UMR 1101, Brest, France
| | - Gwenolé QUELLEC
- Université de Bretagne Occidentale, Brest, France
- LaTIM, INSERM UMR 1101, Brest, France
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Jebril H, Esengönül M, Bogunović H. Anomaly Detection in Optical Coherence Tomography Angiography (OCTA) with a Vector-Quantized Variational Auto-Encoder (VQ-VAE). Bioengineering (Basel) 2024; 11:682. [PMID: 39061764 PMCID: PMC11273395 DOI: 10.3390/bioengineering11070682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/24/2024] [Revised: 06/29/2024] [Accepted: 07/02/2024] [Indexed: 07/28/2024] Open
Abstract
Optical coherence tomography angiography (OCTA) provides detailed information on retinal blood flow and perfusion. Abnormal retinal perfusion indicates possible ocular or systemic disease. We propose a deep learning-based anomaly detection model to identify such anomalies in OCTA. It utilizes two deep learning approaches. First, a representation learning with a Vector-Quantized Variational Auto-Encoder (VQ-VAE) followed by Auto-Regressive (AR) modeling. Second, it exploits epistemic uncertainty estimates from Bayesian U-Net employed to segment the vasculature on OCTA en face images. Evaluation on two large public datasets, DRAC and OCTA-500, demonstrates effective anomaly detection (an AUROC of 0.92 for the DRAC and an AUROC of 0.75 for the OCTA-500) and localization (a mean Dice score of 0.61 for the DRAC) on this challenging task. To our knowledge, this is the first work that addresses anomaly detection in OCTA.
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Affiliation(s)
- Hana Jebril
- Lab for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, 1090 Vienna, Austria; (H.J.); (M.E.)
| | - Meltem Esengönül
- Lab for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, 1090 Vienna, Austria; (H.J.); (M.E.)
| | - Hrvoje Bogunović
- Lab for Ophthalmic Image Analysis, Department of Ophthalmology and Optometry, Medical University of Vienna, 1090 Vienna, Austria; (H.J.); (M.E.)
- Christian Doppler Lab for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, 1090 Vienna, Austria
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Kreitner L, Paetzold JC, Rauch N, Chen C, Hagag AM, Fayed AE, Sivaprasad S, Rausch S, Weichsel J, Menze BH, Harders M, Knier B, Rueckert D, Menten MJ. Synthetic Optical Coherence Tomography Angiographs for Detailed Retinal Vessel Segmentation Without Human Annotations. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2061-2073. [PMID: 38224512 DOI: 10.1109/tmi.2024.3354408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Optical coherence tomography angiography (OCTA) is a non-invasive imaging modality that can acquire high-resolution volumes of the retinal vasculature and aid the diagnosis of ocular, neurological and cardiac diseases. Segmenting the visible blood vessels is a common first step when extracting quantitative biomarkers from these images. Classical segmentation algorithms based on thresholding are strongly affected by image artifacts and limited signal-to-noise ratio. The use of modern, deep learning-based segmentation methods has been inhibited by a lack of large datasets with detailed annotations of the blood vessels. To address this issue, recent work has employed transfer learning, where a segmentation network is trained on synthetic OCTA images and is then applied to real data. However, the previously proposed simulations fail to faithfully model the retinal vasculature and do not provide effective domain adaptation. Because of this, current methods are unable to fully segment the retinal vasculature, in particular the smallest capillaries. In this work, we present a lightweight simulation of the retinal vascular network based on space colonization for faster and more realistic OCTA synthesis. We then introduce three contrast adaptation pipelines to decrease the domain gap between real and artificial images. We demonstrate the superior segmentation performance of our approach in extensive quantitative and qualitative experiments on three public datasets that compare our method to traditional computer vision algorithms and supervised training using human annotations. Finally, we make our entire pipeline publicly available, including the source code, pretrained models, and a large dataset of synthetic OCTA images.
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Wicklein R, Kreitner L, Wild A, Aly L, Rueckert D, Hemmer B, Korn T, Menten MJ, Knier B. Retinal small vessel pathology is associated with disease burden in multiple sclerosis. Mult Scler 2024; 30:812-819. [PMID: 38751230 PMCID: PMC11134992 DOI: 10.1177/13524585241247775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Revised: 03/26/2024] [Accepted: 03/31/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND Alterations of the superficial retinal vasculature are commonly observed in multiple sclerosis (MS) and can be visualized through optical coherence tomography angiography (OCTA). OBJECTIVES This study aimed to examine changes in the retinal vasculature during MS and to integrate findings into current concepts of the underlying pathology. METHODS In this cross-sectional study, including 259 relapsing-remitting MS patients and 78 healthy controls, we analyzed OCTAs using deep-learning-based segmentation algorithm tools. RESULTS We identified a loss of small-sized vessels (diameter < 10 µm) in the superficial vascular complex in all MS eyes, irrespective of their optic neuritis (ON) history. This alteration was associated with MS disease burden and appears independent of retinal ganglion cell loss. In contrast, an observed reduction of medium-sized vessels (diameter 10-20 µm) was specific to eyes with a history of ON and was closely linked to ganglion cell atrophy. CONCLUSION These findings suggest distinct atrophy patterns in retinal vessels in patients with MS. Further studies are necessary to investigate retinal vessel alterations and their underlying pathology in MS.
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Affiliation(s)
- Rebecca Wicklein
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Linus Kreitner
- Institute for AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Anna Wild
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Lilian Aly
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Daniel Rueckert
- Institute for AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- BioMedIA, Imperial College London, London, UK
| | - Bernhard Hemmer
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Thomas Korn
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Institute for Experimental Neuroimmunology, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
| | - Martin J Menten
- Institute for AI and Informatics in Medicine, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- BioMedIA, Imperial College London, London, UK
| | - Benjamin Knier
- Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine and Health, Technical University of Munich, Munich, Germany
- Department of Neurology and Geriatric Neurology, Diakonie Klinikum Schwäbisch Hall, Schwäbisch Hall, Germany
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Fayed AE, Menten MJ, Kreitner L, Paetzold JC, Rueckert D, Bassily SM, Fikry RR, Hagag AM, Sivaprasad S. Retinal vasculature of different diameters and plexuses exhibit distinct vulnerability in varying severity of diabetic retinopathy. Eye (Lond) 2024; 38:1762-1769. [PMID: 38514853 PMCID: PMC11156674 DOI: 10.1038/s41433-024-03021-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/23/2024] Open
Abstract
OBJECTIVES To study the changes in vessel densities (VD) stratified by vessel diameter in the retinal superficial and deep vascular complexes (SVC/DVC) using optical coherence tomography angiography (OCTA) images obtained from people with diabetes and age-matched healthy controls. METHODS We quantified the VD based on vessel diameter categorized as <10, 10-20 and >20 μm in the SVC/DVC obtained on 3 × 3 mm2 OCTA scans using a deep learning-based segmentation and vascular graph extraction tool in people with diabetes and age-matched healthy controls. RESULTS OCTA images obtained from 854 eyes of 854 subjects were divided into 5 groups: healthy controls (n = 555); people with diabetes with no diabetic retinopathy (DR, n = 90), mild and moderate non-proliferative DR (NPDR) (n = 96), severe NPDR (n = 42) and proliferative DR (PDR) (n = 71). Both SVC and DVC showed significant decrease in VD with increasing DR severity (p < 0.001). The largest difference was observed in the <10 μm vessels of the SVC between healthy controls and no DR (13.9% lower in no DR, p < 0.001). Progressive decrease in <10 μm vessels of the SVC and DVC was seen with increasing DR severity (p < 0.001). However, 10-20 μm vessels only showed decline in the DVC, but not the SVC (p < 0.001) and there was no change observed in the >20 μm vessels in either plexus. CONCLUSIONS Our findings suggest that OCTA is able to demonstrate a distinct vulnerability of the smallest retinal vessels in both plexuses that worsens with increasing severity of DR.
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Affiliation(s)
- Alaa E Fayed
- Department of Ophthalmology, Kasr Al-Ainy School of Medicine, Cairo University, Giza, Egypt.
- Watany Eye Hospital, Cairo, Egypt.
- Oxford Eye Hospital, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Martin J Menten
- Lab for AI in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- BioMedIA, Imperial College London, London, UK
| | - Linus Kreitner
- Lab for AI in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Johannes C Paetzold
- Lab for AI in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- BioMedIA, Imperial College London, London, UK
| | - Daniel Rueckert
- Lab for AI in Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- BioMedIA, Imperial College London, London, UK
| | | | - Ramy R Fikry
- Department of Ophthalmology, Kasr Al-Ainy School of Medicine, Cairo University, Giza, Egypt
- Watany Eye Hospital, Cairo, Egypt
| | - Ahmed M Hagag
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- Boehringer Ingelheim Limited, London, UK
| | - Sobha Sivaprasad
- NIHR Moorfields Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust, London, UK
- University College London, London, UK
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Li M, Huang K, Xu Q, Yang J, Zhang Y, Ji Z, Xie K, Yuan S, Liu Q, Chen Q. OCTA-500: A retinal dataset for optical coherence tomography angiography study. Med Image Anal 2024; 93:103092. [PMID: 38325155 DOI: 10.1016/j.media.2024.103092] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 11/10/2023] [Accepted: 01/22/2024] [Indexed: 02/09/2024]
Abstract
Optical coherence tomography angiography (OCTA) is a novel imaging modality that has been widely utilized in ophthalmology and neuroscience studies to observe retinal vessels and microvascular systems. However, publicly available OCTA datasets remain scarce. In this paper, we introduce the largest and most comprehensive OCTA dataset dubbed OCTA-500, which contains OCTA imaging under two fields of view (FOVs) from 500 subjects. The dataset provides rich images and annotations including two modalities (OCT/OCTA volumes), six types of projections, four types of text labels (age/gender/eye/disease) and seven types of segmentation labels (large vessel/capillary/artery/vein/2D FAZ/3D FAZ/retinal layers). Then, we propose a multi-object segmentation task called CAVF, which integrates capillary segmentation, artery segmentation, vein segmentation, and FAZ segmentation under a unified framework. In addition, we optimize the 3D-to-2D image projection network (IPN) to IPN-V2 to serve as one of the segmentation baselines. Experimental results demonstrate that IPN-V2 achieves an about 10% mIoU improvement over IPN on CAVF task. Finally, we further study the impact of several dataset characteristics: the training set size, the model input (OCT/OCTA, 3D volume/2D projection), the baseline networks, and the diseases. The dataset and code are publicly available at: https://ieee-dataport.org/open-access/octa-500.
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Affiliation(s)
- Mingchao Li
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Kun Huang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Qiuzhuo Xu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Jiadong Yang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Yuhan Zhang
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Zexuan Ji
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
| | - Keren Xie
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Songtao Yuan
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Qinghuai Liu
- Department of Ophthalmology, The First Affiliated Hospital with Nanjing Medical University, NanJing 210029, China.
| | - Qiang Chen
- School of Computer Science and Engineering, Nanjing University of Science and Technology, NanJing 210094, China.
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Li Z, Huang G, Zou B, Chen W, Zhang T, Xu Z, Cai K, Wang T, Sun Y, Wang Y, Jin K, Huang X. Segmentation of Low-Light Optical Coherence Tomography Angiography Images under the Constraints of Vascular Network Topology. SENSORS (BASEL, SWITZERLAND) 2024; 24:774. [PMID: 38339491 PMCID: PMC10856982 DOI: 10.3390/s24030774] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Revised: 12/27/2023] [Accepted: 01/03/2024] [Indexed: 02/12/2024]
Abstract
Optical coherence tomography angiography (OCTA) offers critical insights into the retinal vascular system, yet its full potential is hindered by challenges in precise image segmentation. Current methodologies struggle with imaging artifacts and clarity issues, particularly under low-light conditions and when using various high-speed CMOS sensors. These challenges are particularly pronounced when diagnosing and classifying diseases such as branch vein occlusion (BVO). To address these issues, we have developed a novel network based on topological structure generation, which transitions from superficial to deep retinal layers to enhance OCTA segmentation accuracy. Our approach not only demonstrates improved performance through qualitative visual comparisons and quantitative metric analyses but also effectively mitigates artifacts caused by low-light OCTA, resulting in reduced noise and enhanced clarity of the images. Furthermore, our system introduces a structured methodology for classifying BVO diseases, bridging a critical gap in this field. The primary aim of these advancements is to elevate the quality of OCTA images and bolster the reliability of their segmentation. Initial evaluations suggest that our method holds promise for establishing robust, fine-grained standards in OCTA vascular segmentation and analysis.
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Affiliation(s)
- Zhi Li
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
| | - Gaopeng Huang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
| | - Binfeng Zou
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
| | - Wenhao Chen
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
| | - Tianyun Zhang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
| | - Zhaoyang Xu
- Department of Paediatrics, University of Cambridge, Cambridge CB2 1TN, UK;
| | - Kunyan Cai
- Faculty of Applied Sciences, Macao Polytechnic University, Macao SAR 999078, China;
| | - Tingyu Wang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
| | - Yaoqi Sun
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
- Lishui Institute, Hangzhou Dianzi University, Lishui 323000, China
| | - Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou 310018, China;
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310027, China;
| | - Xingru Huang
- School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China; (Z.L.); (G.H.); (B.Z.); (W.C.); (T.Z.); (T.W.); (Y.S.)
- School of Electronic Engineering and Computer Science, Queen Mary University of London, London E3 4BL, UK
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Pradeep K, Jeyakumar V, Bhende M, Shakeel A, Mahadevan S. Artificial intelligence and hemodynamic studies in optical coherence tomography angiography for diabetic retinopathy evaluation: A review. Proc Inst Mech Eng H 2024; 238:3-21. [PMID: 38044619 DOI: 10.1177/09544119231213443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2023]
Abstract
Diabetic retinopathy (DR) is a rapidly emerging retinal abnormality worldwide, which can cause significant vision loss by disrupting the vascular structure in the retina. Recently, optical coherence tomography angiography (OCTA) has emerged as an effective imaging tool for diagnosing and monitoring DR. OCTA produces high-quality 3-dimensional images and provides deeper visualization of retinal vessel capillaries and plexuses. The clinical relevance of OCTA in detecting, classifying, and planning therapeutic procedures for DR patients has been highlighted in various studies. Quantitative indicators obtained from OCTA, such as blood vessel segmentation of the retina, foveal avascular zone (FAZ) extraction, retinal blood vessel density, blood velocity, flow rate, capillary vessel pressure, and retinal oxygen extraction, have been identified as crucial hemodynamic features for screening DR using computer-aided systems in artificial intelligence (AI). AI has the potential to assist physicians and ophthalmologists in developing new treatment options. In this review, we explore how OCTA has impacted the future of DR screening and early diagnosis. It also focuses on how analysis methods have evolved over time in clinical trials. The future of OCTA imaging and its continued use in AI-assisted analysis is promising and will undoubtedly enhance the clinical management of DR.
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Affiliation(s)
- K Pradeep
- Department of Biomedical Engineering, Chennai Institute of Technology, Chennai, Tamil Nadu, India
| | - Vijay Jeyakumar
- Department of Biomedical Engineering, Sri Sivasubramaniya Nadar College of Engineering, Chennai, Tamil Nadu, India
| | - Muna Bhende
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya Medical Research Foundation, Chennai, Tamil Nadu, India
| | - Areeba Shakeel
- Vitreoretina Department, Sankara Nethralaya Medical Research Foundation, Chennai, Tamil Nadu, India
| | - Shriraam Mahadevan
- Department of Endocrinology, Sri Ramachandra Institute of Higher Education and Research, Chennai, Tamil Nadu, India
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Li M, Huang K, Zeng C, Chen Q, Zhang W. Visualization and quantization of 3D retinal vessels in OCTA images. OPTICS EXPRESS 2024; 32:471-481. [PMID: 38175076 DOI: 10.1364/oe.504877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 12/06/2023] [Indexed: 01/05/2024]
Abstract
Optical coherence tomography angiography (OCTA) has been increasingly used in the analysis of ophthalmic diseases in recent years. Automatic vessel segmentation in 2D OCTA projection images is commonly used in clinical practice. However, OCTA provides a 3D volume of the retinal blood vessels with rich spatial distribution information, and it is incomplete to segment retinal vessels only in 2D projection images. Here, considering that it is difficult to manually label 3D vessels, we introduce a 3D vessel segmentation and reconstruction method for OCTA images with only 2D vessel labels. We implemented 3D vessel segmentation in the OCTA volume using a specially trained 2D vessel segmentation model. The 3D vessel segmentation results are further used to calculate 3D vessel parameters and perform 3D reconstruction. The experimental results on the public dataset OCTA-500 demonstrate that 3D vessel parameters have higher sensitivity to vascular alteration than 2D vessel parameters, which makes it meaningful for clinical analysis. The 3D vessel reconstruction provides vascular visualization in different retinal layers that can be used to monitor the development of retinal diseases. Finally, we also illustrate the use of 3D reconstruction results to determine the relationship between the location of arteries and veins.
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Schmetterer L, Scholl H, Garhöfer G, Janeschitz-Kriegl L, Corvi F, Sadda SR, Medeiros FA. Endpoints for clinical trials in ophthalmology. Prog Retin Eye Res 2023; 97:101160. [PMID: 36599784 DOI: 10.1016/j.preteyeres.2022.101160] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 12/22/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023]
Abstract
With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.
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Affiliation(s)
- Leopold Schmetterer
- Singapore Eye Research Institute, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore; Academic Clinical Program, Duke-NUS Medical School, Singapore; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore; Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.
| | - Hendrik Scholl
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Gerhard Garhöfer
- Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria
| | - Lucas Janeschitz-Kriegl
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Federico Corvi
- Eye Clinic, Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Italy
| | - SriniVas R Sadda
- Doheny Eye Institute, Los Angeles, CA, USA; Department of Ophthalmology, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Felipe A Medeiros
- Vision, Imaging and Performance Laboratory, Department of Ophthalmology, Duke Eye Center, Duke University, Durham, NC, USA
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Liao J, Yang S, Zhang T, Li C, Huang Z. Fast optical coherence tomography angiography image acquisition and reconstruction pipeline for skin application. BIOMEDICAL OPTICS EXPRESS 2023; 14:3899-3913. [PMID: 37799685 PMCID: PMC10549725 DOI: 10.1364/boe.486933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 04/19/2023] [Accepted: 04/19/2023] [Indexed: 10/07/2023]
Abstract
Traditional high-quality OCTA images require multi-repeated scans (e.g., 4-8 repeats) in the same position, which may cause the patient to be uncomfortable. We propose a deep-learning-based pipeline that can extract high-quality OCTA images from only two-repeat OCT scans. The performance of the proposed image reconstruction U-Net (IRU-Net) outperforms the state-of-the-art UNet vision transformer and UNet in OCTA image reconstruction from a two-repeat OCT signal. The results demonstrated a mean peak-signal-to-noise ratio increased from 15.7 to 24.2; the mean structural similarity index measure improved from 0.28 to 0.59, while the OCT data acquisition time was reduced from 21 seconds to 3.5 seconds (reduced by 83%).
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Affiliation(s)
- Jinpeng Liao
- School of Science and Engineering,
University of Dundee, DD1 4HN, Scotland, UK
| | - Shufan Yang
- Engineering and Built Environment, Edinburgh Napier University, Edinburgh, UK
- Research Department of Orthopaedics and Musculoskeletal Science, University College London, UK
| | - Tianyu Zhang
- School of Science and Engineering,
University of Dundee, DD1 4HN, Scotland, UK
| | - Chunhui Li
- School of Science and Engineering,
University of Dundee, DD1 4HN, Scotland, UK
| | - Zhihong Huang
- School of Science and Engineering,
University of Dundee, DD1 4HN, Scotland, UK
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12
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Tan X, Chen X, Meng Q, Shi F, Xiang D, Chen Z, Pan L, Zhu W. OCT 2Former: A retinal OCT-angiography vessel segmentation transformer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 233:107454. [PMID: 36921468 DOI: 10.1016/j.cmpb.2023.107454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 01/25/2023] [Accepted: 02/27/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND AND OBJECTIVE Retinal vessel segmentation plays an important role in the automatic retinal disease screening and diagnosis. How to segment thin vessels and maintain the connectivity of vessels are the key challenges of the retinal vessel segmentation task. Optical coherence tomography angiography (OCTA) is a noninvasive imaging technique that can reveal high-resolution retinal vessels. Aiming at make full use of its characteristic of high resolution, a new end-to-end transformer based network named as OCT2Former (OCT-a Transformer) is proposed to segment retinal vessel accurately in OCTA images. METHODS The proposed OCT2Former is based on encoder-decoder structure, which mainly includes dynamic transformer encoder and lightweight decoder. Dynamic transformer encoder consists of dynamic token aggregation transformer and auxiliary convolution branch, in which the multi-head dynamic token aggregation attention based dynamic token aggregation transformer is designed to capture the global retinal vessel context information from the first layer throughout the network and the auxiliary convolution branch is proposed to compensate for the lack of inductive bias of the transformer and assist in the efficient feature extraction. A convolution based lightweight decoder is proposed to decode features efficiently and reduce the complexity of the proposed OCT2Former. RESULTS The proposed OCT2Former is validated on three publicly available datasets i.e. OCTA-SS, ROSE-1, OCTA-500 (subset OCTA-6M and OCTA-3M). The Jaccard indexes of the proposed OCT2Former on these datasets are 0.8344, 0.7855, 0.8099 and 0.8513, respectively, outperforming the best convolution based network 1.43, 1.32, 0.75 and 1.46%, respectively. CONCLUSION The experimental results have demonstrated that the proposed OCT2Former can achieve competitive performance on retinal OCTA vessel segmentation tasks.
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Affiliation(s)
- Xiao Tan
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Xinjian Chen
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China; The State Key Laboratory of Radiation Medicine and Protection, Soochow University, Jiangsu, China
| | - Qingquan Meng
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Fei Shi
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Dehui Xiang
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Zhongyue Chen
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China
| | - Lingjiao Pan
- School of Electrical and Information Engineering, Jiangsu University of Technology, Jiangsu, China
| | - Weifang Zhu
- MIPAV Lab, the School of Electronic and Information Engineering, Soochow University, Jiangsu, China.
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13
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Prangel D, Prasuhn M, Rommel F, Grisanti S, Ranjbar M. Comparison of Automated Thresholding Algorithms in Optical Coherence Tomography Angiography Image Analysis. J Clin Med 2023; 12:jcm12051973. [PMID: 36902761 PMCID: PMC10004628 DOI: 10.3390/jcm12051973] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 02/09/2023] [Accepted: 02/26/2023] [Indexed: 03/06/2023] Open
Abstract
(1) Background: Calculation of vessel density in optical coherence tomography angiography (OCTA) images with thresholding algorithms varies in clinical routine. The ability to discriminate healthy from diseased eyes based on perfusion of the posterior pole is critical and may depend on the algorithm applied. This study assessed comparability, reliability, and ability in the discrimination of commonly used automated thresholding algorithms. (2) Methods: Vessel density in full retina and choriocapillaris slabs were calculated with five previously published automated thresholding algorithms (Default, Huang, ISODATA, Mean, and Otsu) for healthy and diseased eyes. The algorithms were investigated with LD-F2-analysis for intra-algorithm reliability, agreement, and the ability to discriminate between physiological and pathological conditions. (3) Results: LD-F2-analyses revealed significant differences in estimated vessel densities for the algorithms (p < 0.001). For full retina and choriocapillaris slabs, intra-algorithm values range from excellent to poor, depending on the applied algorithm; the inter-algorithm agreement was low. Discrimination was good for the full retina slabs, but poor when applied to the choriocapillaris slabs. The Mean algorithm demonstrated an overall good performance. (4) Conclusions: Automated threshold algorithms are not interchangeable. The ability for discrimination depends on the analyzed layer. Concerning the full retina slab, all of the five evaluated automated algorithms had an overall good ability for discrimination. When analyzing the choriocapillaris, it might be useful to consider another algorithm.
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Affiliation(s)
- David Prangel
- Laboratory for Angiogenesis & Ocular Cell Transplantation, Ratzeburger Allee 160, 23538 Lübeck, Germany
| | - Michelle Prasuhn
- Laboratory for Angiogenesis & Ocular Cell Transplantation, Ratzeburger Allee 160, 23538 Lübeck, Germany
- Department of Ophthalmology, University Hospital Schleswig-Holstein, University of Luebeck, Ratzeburger Allee 160, 23538 Luebeck, Germany
- Correspondence:
| | - Felix Rommel
- Laboratory for Angiogenesis & Ocular Cell Transplantation, Ratzeburger Allee 160, 23538 Lübeck, Germany
- Department of Ophthalmology, University Hospital Schleswig-Holstein, University of Luebeck, Ratzeburger Allee 160, 23538 Luebeck, Germany
| | - Salvatore Grisanti
- Department of Ophthalmology, University Hospital Schleswig-Holstein, University of Luebeck, Ratzeburger Allee 160, 23538 Luebeck, Germany
| | - Mahdy Ranjbar
- Laboratory for Angiogenesis & Ocular Cell Transplantation, Ratzeburger Allee 160, 23538 Lübeck, Germany
- Department of Ophthalmology, University Hospital Schleswig-Holstein, University of Luebeck, Ratzeburger Allee 160, 23538 Luebeck, Germany
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14
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Xia X, Qin Q, Peng Y, Wang M, Yin Y, Tang Y. Retinal Examinations Provides Early Warning of Alzheimer's Disease. J Alzheimers Dis 2022; 90:1341-1357. [PMID: 36245377 DOI: 10.3233/jad-220596] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Patients with Alzheimer's disease have difficulty maintaining independent living abilities as the disease progresses, causing an increased burden of care on family caregivers and the healthcare system and related financial strain. This patient group is expected to continue to expand as life expectancy climbs. Current diagnostics for Alzheimer's disease are complex, unaffordable, and invasive without regard to diagnosis quality at early stages, which urgently calls for more technical improvements for diagnosis specificity. Optical coherence tomography or tomographic angiography has been shown to identify retinal thickness loss and lower vascular density present earlier than symptom onset in these patients. The retina is an extension of the central nervous system and shares anatomic and functional similarities with the brain. Ophthalmological examinations can be an efficient tool to offer a window into cerebral pathology with the merit of easy operation. In this review, we summarized the latest observations on retinal pathology in Alzheimer's disease and discussed the feasibility of retinal imaging in diagnostic prediction, as well as limitations in current retinal examinations for Alzheimer's disease diagnosis.
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Affiliation(s)
- Xinyi Xia
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Qi Qin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China.,National Center for Neurological Disorders, National Clinical Research Center for Geriatric Diseases, Beijing, China
| | - Yankun Peng
- Department of Ophthalmology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Meng Wang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yunsi Yin
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Yi Tang
- Innovation Center for Neurological Disorders, Department of Neurology, Xuanwu Hospital, Capital Medical University, Beijing, China
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Li H, Tang Z, Nan Y, Yang G. Human treelike tubular structure segmentation: A comprehensive review and future perspectives. Comput Biol Med 2022; 151:106241. [PMID: 36379190 DOI: 10.1016/j.compbiomed.2022.106241] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 09/16/2022] [Accepted: 10/22/2022] [Indexed: 12/27/2022]
Abstract
Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.
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Affiliation(s)
- Hao Li
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Zeyu Tang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Department of Bioengineering, Faculty of Engineering, Imperial College London, London, United Kingdom
| | - Yang Nan
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom
| | - Guang Yang
- National Heart and Lung Institute, Faculty of Medicine, Imperial College London, London, United Kingdom; Royal Brompton Hospital, London, United Kingdom.
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Hao J, Shen T, Zhu X, Liu Y, Behera A, Zhang D, Chen B, Liu J, Zhang J, Zhao Y. Retinal Structure Detection in OCTA Image via Voting-Based Multitask Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3969-3980. [PMID: 36044489 DOI: 10.1109/tmi.2022.3202183] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Automated detection of retinal structures, such as retinal vessels (RV), the foveal avascular zone (FAZ), and retinal vascular junctions (RVJ), are of great importance for understanding diseases of the eye and clinical decision-making. In this paper, we propose a novel Voting-based Adaptive Feature Fusion multi-task network (VAFF-Net) for joint segmentation, detection, and classification of RV, FAZ, and RVJ in optical coherence tomography angiography (OCTA). A task-specific voting gate module is proposed to adaptively extract and fuse different features for specific tasks at two levels: features at different spatial positions from a single encoder, and features from multiple encoders. In particular, since the complexity of the microvasculature in OCTA images makes simultaneous precise localization and classification of retinal vascular junctions into bifurcation/crossing a challenging task, we specifically design a task head by combining the heatmap regression and grid classification. We take advantage of three different en face angiograms from various retinal layers, rather than following existing methods that use only a single en face. We carry out extensive experiments on three OCTA datasets acquired using different imaging devices, and the results demonstrate that the proposed method performs on the whole better than either the state-of-the-art single-purpose methods or existing multi-task learning solutions. We also demonstrate that our multi-task learning method generalizes across other imaging modalities, such as color fundus photography, and may potentially be used as a general multi-task learning tool. We also construct three datasets for multiple structure detection, and part of these datasets with the source code and evaluation benchmark have been released for public access.
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17
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Falavarjani KG, Anvari P, Alemzadeh SA, Moghaddam MMJ, Habibi A, Ashrafkhorasani M. Segmentation Error Correction of the Optical Coherence Tomography Angiography Images in Peer-Reviewed Studies. J Curr Ophthalmol 2022; 34:273-276. [PMID: 36644458 PMCID: PMC9832458 DOI: 10.4103/joco.joco_174_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Revised: 08/04/2022] [Accepted: 08/17/2022] [Indexed: 12/12/2022] Open
Abstract
Purpose To assess the percentage of published articles reporting optical coherence tomography angiography (OCTA) metrics regarding the report of segmentation error correction. Methods A comprehensive search was conducted using the PubMed database for articles on OCTA imaging published between January 1, 2015, and January 1, 2021. All original articles reporting at least one of the OCTA metrics were extracted. The article text was reviewed for the segmentation correction strategy. In addition, the number of articles that mentioned the lack of segmentation error correction as a limitation of the study was recorded. Results From the initial 5288 articles, 1559 articles were included for detailed review. One hundred ninety-six articles (12.57%) used manual correction for segmentation errors. Of the remaining articles, 589 articles (37.8%) excluded images with significant segmentation errors, and 99 articles (6.3%) mentioned segmentation errors as a limitation of their study. The rest of the articles (675, 43.3%) did not address the segmentation error. Multiple logistic regression analysis revealed that ignorance of segmentation error was significantly associated with lower journal ranks, earlier years of publication and disease category of age-related macular degeneration, and glaucoma (all P < 0.001). Conclusions A significant proportion of peer-reviewed articles in PubMed, disregarded the segmentation error correction. The conclusions of such studies should be interpreted with caution. Editors, reviewers, and authors of OCTA articles should pay special attention to the correction of segmentation errors.
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Affiliation(s)
- Khalil Ghasemi Falavarjani
- Eye Research Center, Department of Ophthalmology, The Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran,Stem Cell and Regenerative Medicine Research Center, Iran University of Medical Sciences, Tehran, Iran,Address for correspondence: Khalil Ghasemi Falavarjani, Eye Research Center, Rassoul Akram Hospital, Sattarkhan-Niyayesh St, Tehran, Iran. E-mail:
| | - Pasha Anvari
- Eye Research Center, Department of Ophthalmology, The Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Sayyed Amirpooya Alemzadeh
- Eye Research Center, Department of Ophthalmology, The Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Mohammad Mehdi Johari Moghaddam
- Eye Research Center, Department of Ophthalmology, The Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Abbas Habibi
- Eye Research Center, Department of Ophthalmology, The Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
| | - Maryam Ashrafkhorasani
- Eye Research Center, Department of Ophthalmology, The Five Senses Health Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran
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18
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Tsang KCH, Pinnock H, Wilson AM, Shah SA. Application of Machine Learning Algorithms for Asthma Management with mHealth: A Clinical Review. J Asthma Allergy 2022; 15:855-873. [PMID: 35791395 PMCID: PMC9250768 DOI: 10.2147/jaa.s285742] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2021] [Accepted: 06/16/2022] [Indexed: 12/21/2022] Open
Abstract
Background Asthma is a variable long-term condition. Currently, there is no cure for asthma and the focus is, therefore, on long-term management. Mobile health (mHealth) is promising for chronic disease management but to be able to realize its potential, it needs to go beyond simply monitoring. mHealth therefore needs to leverage machine learning to provide tailored feedback with personalized algorithms. There is a need to understand the extent of machine learning that has been leveraged in the context of mHealth for asthma management. This review aims to fill this gap. Methods We searched PubMed for peer-reviewed studies that applied machine learning to data derived from mHealth for asthma management in the last five years. We selected studies that included some human data other than routinely collected in primary care and used at least one machine learning algorithm. Results Out of 90 studies, we identified 22 relevant studies that were then further reviewed. Broadly, existing research efforts can be categorized into three types: 1) technology development, 2) attack prediction, 3) patient clustering. Using data from a variety of devices (smartphones, smartwatches, peak flow meters, electronic noses, smart inhalers, and pulse oximeters), most applications used supervised learning algorithms (logistic regression, decision trees, and related algorithms) while a few used unsupervised learning algorithms. The vast majority used traditional machine learning techniques, but a few studies investigated the use of deep learning algorithms. Discussion In the past five years, many studies have successfully applied machine learning to asthma mHealth data. However, most have been developed on small datasets with internal validation at best. Small sample sizes and lack of external validation limit the generalizability of these studies. Future research should collect data that are more representative of the wider asthma population and focus on validating the derived algorithms and technologies in a real-world setting.
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Affiliation(s)
- Kevin C H Tsang
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Hilary Pinnock
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
| | - Andrew M Wilson
- Asthma UK Centre for Applied Research, and Norwich Medical School, University of East Anglia, Norwich, UK
| | - Syed Ahmar Shah
- Asthma UK Centre for Applied Research, Usher Institute, University of Edinburgh, Edinburgh, UK
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19
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Sampson DM, Dubis AM, Chen FK, Zawadzki RJ, Sampson DD. Towards standardizing retinal optical coherence tomography angiography: a review. LIGHT, SCIENCE & APPLICATIONS 2022; 11:63. [PMID: 35304441 PMCID: PMC8933532 DOI: 10.1038/s41377-022-00740-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 05/11/2023]
Abstract
The visualization and assessment of retinal microvasculature are important in the study, diagnosis, monitoring, and guidance of treatment of ocular and systemic diseases. With the introduction of optical coherence tomography angiography (OCTA), it has become possible to visualize the retinal microvasculature volumetrically and without a contrast agent. Many lab-based and commercial clinical instruments, imaging protocols and data analysis methods and metrics, have been applied, often inconsistently, resulting in a confusing picture that represents a major barrier to progress in applying OCTA to reduce the burden of disease. Open data and software sharing, and cross-comparison and pooling of data from different studies are rare. These inabilities have impeded building the large databases of annotated OCTA images of healthy and diseased retinas that are necessary to study and define characteristics of specific conditions. This paper addresses the steps needed to standardize OCTA imaging of the human retina to address these limitations. Through review of the OCTA literature, we identify issues and inconsistencies and propose minimum standards for imaging protocols, data analysis methods, metrics, reporting of findings, and clinical practice and, where this is not possible, we identify areas that require further investigation. We hope that this paper will encourage the unification of imaging protocols in OCTA, promote transparency in the process of data collection, analysis, and reporting, and facilitate increasing the impact of OCTA on retinal healthcare delivery and life science investigations.
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Affiliation(s)
- Danuta M Sampson
- Surrey Biophotonics, Centre for Vision, Speech and Signal Processing and School of Biosciences and Medicine, The University of Surrey, Guildford, GU2 7XH, UK.
| | - Adam M Dubis
- NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Trust and UCL Institute of Ophthalmology, London, EC1V 2PD, UK
| | - Fred K Chen
- Centre for Ophthalmology and Visual Science (incorporating Lions Eye Institute), The University of Western Australia, Nedlands, Western Australia, 6009, Australia
- Department of Ophthalmology, Royal Perth Hospital, Perth, Western Australia, 6000, Australia
- Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Victoria, 3002, Australia
| | - Robert J Zawadzki
- Department of Ophthalmology & Vision Science, University of California Davis, Sacramento, CA, 95817, USA
| | - David D Sampson
- Surrey Biophotonics, Advanced Technology Institute, School of Physics and School of Biosciences and Medicine, University of Surrey, Guildford, Surrey, GU2 7XH, UK
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Role of Anterior Segment-Optical Coherence Tomography Angiography in Acute Ocular Burns. Diagnostics (Basel) 2022; 12:diagnostics12030607. [PMID: 35328160 PMCID: PMC8947509 DOI: 10.3390/diagnostics12030607] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 02/09/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
Acute ocular burns have varied manifestations which require prompt diagnosis and management to prevent chronic sequelae. Of these, the detection of limbal ischemia poses a challenge because of the subjective nature of its clinical signs. Anterior segment optical coherence tomography angiography (AS-OCTA) offers an objective method of assessing ischemia in these eyes. This review provides an overview of the technology of AS-OCTA and its applications in acute burns. AS-OCTA generates images by isolating the movement of erythrocytes within blood vessels from sequentially obtained b-scans. Limbal ischemia manifests in these scans as absent vasculature and the extent of ischemia can be quantified using different vessel-related parameters. Of these, the density of vessels is most commonly used and correlates with the severity of the injury. Incorporation of the degree of ischemia in the classification of acute burns has been attempted in animal studies and its extension to human trials may provide an added dimension in determining the final prognosis of these eyes. Thus, AS-OCTA is a promising device that can objectively evaluate limbal ischemia. This will facilitate the identification of patients who will benefit from revascularization therapies and stem cell transplants in acute and chronic ocular burns, respectively.
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