1
|
Engelmann J, Burke J, Hamid C, Reid-Schachter M, Pugh D, Dhaun N, Moukaddem D, Gray L, Strang N, McGraw P, Storkey A, Steptoe PJ, King S, MacGillivray T, Bernabeu MO, MacCormick IJC. Choroidalyzer: An Open-Source, End-to-End Pipeline for Choroidal Analysis in Optical Coherence Tomography. Invest Ophthalmol Vis Sci 2024; 65:6. [PMID: 38833259 PMCID: PMC11156207 DOI: 10.1167/iovs.65.6.6] [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: 12/05/2023] [Accepted: 04/22/2024] [Indexed: 06/06/2024] Open
Abstract
Purpose To develop Choroidalyzer, an open-source, end-to-end pipeline for segmenting the choroid region, vessels, and fovea, and deriving choroidal thickness, area, and vascular index. Methods We used 5600 OCT B-scans (233 subjects, six systemic disease cohorts, three device types, two manufacturers). To generate region and vessel ground-truths, we used state-of-the-art automatic methods following manual correction of inaccurate segmentations, with foveal positions manually annotated. We trained a U-Net deep learning model to detect the region, vessels, and fovea to calculate choroid thickness, area, and vascular index in a fovea-centered region of interest. We analyzed segmentation agreement (AUC, Dice) and choroid metrics agreement (Pearson, Spearman, mean absolute error [MAE]) in internal and external test sets. We compared Choroidalyzer to two manual graders on a small subset of external test images and examined cases of high error. Results Choroidalyzer took 0.299 seconds per image on a standard laptop and achieved excellent region (Dice: internal 0.9789, external 0.9749), very good vessel segmentation performance (Dice: internal 0.8817, external 0.8703), and excellent fovea location prediction (MAE: internal 3.9 pixels, external 3.4 pixels). For thickness, area, and vascular index, Pearson correlations were 0.9754, 0.9815, and 0.8285 (internal)/0.9831, 0.9779, 0.7948 (external), respectively (all P < 0.0001). Choroidalyzer's agreement with graders was comparable to the intergrader agreement across all metrics. Conclusions Choroidalyzer is an open-source, end-to-end pipeline that accurately segments the choroid and reliably extracts thickness, area, and vascular index. Especially choroidal vessel segmentation is a difficult and subjective task, and fully automatic methods like Choroidalyzer could provide objectivity and standardization.
Collapse
Affiliation(s)
- Justin Engelmann
- School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Medical Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Jamie Burke
- School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom
| | - Charlene Hamid
- Clinical Research Facility and Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Megan Reid-Schachter
- Clinical Research Facility and Imaging, University of Edinburgh, Edinburgh, United Kingdom
| | - Dan Pugh
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Neeraj Dhaun
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Diana Moukaddem
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, United Kingdom
| | - Lyle Gray
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, United Kingdom
| | - Niall Strang
- Department of Vision Sciences, Glasgow Caledonian University, Glasgow, United Kingdom
| | - Paul McGraw
- School of Psychology, University of Nottingham, Nottingham, United Kingdom
| | - Amos Storkey
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
| | - Paul J. Steptoe
- Princess Alexandra Eye Pavilion, NHS Lothian, Edinburgh, United Kingdom
| | - Stuart King
- School of Mathematics, University of Edinburgh, Edinburgh, United Kingdom
| | - Tom MacGillivray
- Clinical Research Facility and Imaging, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| | - Miguel O. Bernabeu
- Centre for Medical Informatics, University of Edinburgh, Edinburgh, United Kingdom
- The Bayes Centre, University of Edinburgh, Edinburgh, United Kingdom
| | - Ian J. C. MacCormick
- Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, United Kingdom
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, United Kingdom
| |
Collapse
|
2
|
Deep learning based diagnostic quality assessment of choroidal OCT features with expert-evaluated explainability. Sci Rep 2023; 13:1570. [PMID: 36709332 PMCID: PMC9884235 DOI: 10.1038/s41598-023-28512-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Accepted: 01/19/2023] [Indexed: 01/30/2023] Open
Abstract
Various vision-threatening eye diseases including age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR) are caused due to the dysfunctions manifested in the highly vascular choroid layer of the posterior segment of the eye. In the current clinical practice, screening choroidal structural changes is widely based on optical coherence tomography (OCT) images. Accordingly, to assist clinicians, several automated choroidal biomarker detection methods using OCT images are developed. However, the performance of these algorithms is largely constrained by the quality of the OCT scan. Consequently, determining the quality of choroidal features in OCT scans is significant in building standardized quantification tools and hence constitutes our main objective. This study includes a dataset of 1593 good and 2581 bad quality Spectralis OCT images graded by an expert. Noting the efficacy of deep-learning (DL) in medical image analysis, we propose to train three state-of-the-art DL models: ResNet18, EfficientNet-B0 and EfficientNet-B3 to detect the quality of OCT images. The choice of these models was inspired by their ability to preserve the salient features across all the layers without information loss. To evaluate the attention of DL models on the choroid, we introduced color transparency maps (CTMs) based on GradCAM explanations. Further, we proposed two subjective grading scores: overall choroid coverage (OCC) and choroid coverage in the visible region(CCVR) based on CTMs to objectively correlate visual explanations vis-à-vis DL model attentions. We observed that the average accuracy and F-scores for the three DL models are greater than 96%. Further, the OCC and CCVR scores achieved for the three DL models under consideration substantiate that they mostly focus on the choroid layer in making the decision. In particular, of the three DL models, EfficientNet-B3 is in close agreement with the clinician's inference. The proposed DL-based framework demonstrated high detection accuracy as well as attention on the choroid layer, where EfficientNet-B3 reported superior performance. Our work assumes significance in bench-marking the automated choroid biomarker detection tools and facilitating high-throughput screening. Further, the methods proposed in this work can be adopted for evaluating the attention of DL-based approaches developed for other region-specific quality assessment tasks.
Collapse
|
3
|
Sekiryu T. Choroidal imaging using optical coherence tomography: techniques and interpretations. Jpn J Ophthalmol 2022; 66:213-226. [PMID: 35171356 DOI: 10.1007/s10384-022-00902-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Accepted: 12/23/2021] [Indexed: 02/05/2023]
Abstract
The choroid is vascularized membranous tissue that supplies oxygen and nutrients to the photoreceptors and outer retina. Choroidal vessels underlying the retinal pigment epithelium are difficult to visualize by ophthalmoscopy and slit-lamp examinations. Optical coherence tomography (OCT) imaging made significant advancements in the last 2 decades; it allows visualization of the choroid and its vasculature. Enhanced-depth imaging techniques and swept-source OCT provide detailed choroidal images. A recent breakthrough, OCT angiography (OCTA), visualizes blood flow in the choriocapillaris. However, despite using OCTA, it is hard to visualize the choroidal vessel blood flow. In conventional structural OCT the choroidal vessel structure appears as a low-intensity objects. Image-processing techniques help obtain structural information about these vessels. Manual or automated segmentation of the choroid and binarization techniques enable evaluation of choroidal vessels. Viewing the three-dimensional choroidal vasculature is also possible using high-scan speed volumetric OCT. Unfortunately, although choroidal image analyses are possible using the images obtained by commercially available OCT, the built-in function that analyzes the choroidal vasculature may be insufficient to perform quantitative imaging analysis. Physicians must do that themselves. This review summarizes recent choroidal imaging processing techniques and explains the interpretation of the results for the benefit of imaging experts and ophthalmologists alike.
Collapse
Affiliation(s)
- Tetsuju Sekiryu
- Department of Ophthalmology, Fukushima Medical University, 1 Hikarigaoka, Fukushima, Fukushima, 960-1295, Japan.
| |
Collapse
|
4
|
Liu X, Bi L, Xu Y, Feng D, Kim J, Xu X. Robust deep learning method for choroidal vessel segmentation on swept source optical coherence tomography images. BIOMEDICAL OPTICS EXPRESS 2019; 10:1601-1612. [PMID: 31061759 PMCID: PMC6485000 DOI: 10.1364/boe.10.001601] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 02/13/2019] [Accepted: 02/13/2019] [Indexed: 05/06/2023]
Abstract
Accurate choroidal vessel segmentation with swept-source optical coherence tomography (SS-OCT) images provide unprecedented quantitative analysis towards the understanding of choroid-related diseases. Motivated by the leading segmentation performance in medical images from the use of deep learning methods, in this study, we proposed the adoption of a deep learning method, RefineNet, to segment the choroidal vessels from SS-OCT images. We quantitatively evaluated the RefineNet on 40 SS-OCT images consisting of ~3,900 manually annotated choroidal vessels regions. We achieved a segmentation agreement (SA) of 0.840 ± 0.035 with clinician 1 (C1) and 0.823 ± 0.027 with clinician 2 (C2). These results were higher than inter-observer variability measure in SA between C1 and C2 of 0.821 ± 0.037. Our results demonstrated that the choroidal vessels from SS-OCT can be automatically segmented using a deep learning method and thus provided a new approach towards an objective and reproducible quantitative analysis of vessel regions.
Collapse
Affiliation(s)
- Xiaoxiao Liu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
- Shanghai Key Laboratory of Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080, China
| | - Lei Bi
- Biomedical and Multimedia Information Technology (BMIT) Research Group, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Yupeng Xu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
- Shanghai Key Laboratory of Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080, China
| | - Dagan Feng
- Shanghai Key Laboratory of Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080, China
| | - Jinman Kim
- Biomedical and Multimedia Information Technology (BMIT) Research Group, The University of Sydney, Sydney, NSW, 2006, Australia
| | - Xun Xu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China
| |
Collapse
|