Structured layer surface segmentation for retina OCT using fully convolutional regression networks
Open accessAbstract
Optical coherence tomography (OCT) is a noninvasive imaging modality with micrometer resolution which has been widely used for scanning the retina. Retinal layers are important biomarkers for many diseases. Accurate automated algorithms for segmenting smooth continuous layer surfaces with correct hierarchy (topology) are important for automated retinal thickness and surface shape analysis. State-of-the-art methods typically use a two step process. Firstly, a trained classifier is used to label each pixel into either background and layers or boundaries and non-boundaries. Secondly, the desired smooth surfaces with the correct topology are extracted by graph methods (e.g., graph cut). Data driven methods like deep networks have shown great ability for the pixel classification step, but to date have not been able to extract structured smooth continuous surfaces with topological constraints in the second step. In this paper, we combine these two steps into a unified deep learning framework by directly modeling the distribution of the surface positions. Smooth, continuous, and topologically correct surfaces are obtained in a single feed forward operation. The proposed method was evaluated on two publicly available data sets of healthy controls and subjects with either multiple sclerosis or diabetic macular edema, and is shown to achieve state-of-the art performance with sub-pixel accuracy.
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On the DME dataset, the method from He et al. [15] achieved an average absolute distance of 6.70 μm.
“Average | 12.01 6.29 | 7.37 0.93 | 7.34 1.08 | 6.70 | 6.74 4.10”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [15]
The Experiments section describes the DME dataset, and “Surface position prediction accuracy” says the method’s boundary mean absolute distance (MAD) is reported in Table 2. The passages available from our copy do not give the Table 2 value, so they do not verify the claimed average of 6.70 μm. (LLM, conf=0.77)
Experimental — accuracy not yet measuredFor the HC/MS dataset, a single click per layer already achieves a performance better than the state-of-the-art, whereas with manual correction more than 15 clicks per layer would be required.
“For instance, for the HC/MS dataset (Fig 5a) a single click per layer already achieves a performance better than the state-of-the-art [15], whereas with the manual correction more than 15 clicks per layer would be required.”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [15]
The deep residual U-Net has already been successfully used for retinal OCT images.
“In this study, we opt for a deep residual U-Net [15], [44], which has already been successfully used for retinal OCT images.”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [15]
Pixel-wise segmentation approaches tend to underperform on retinal boundary segmentation in OCT images because they do not properly encode anatomical priors such as layer ordering, allowing segmented layers to occur at unreasonable locations.
“Indeed, pixel-wise segmentation approaches tend to underperform on retinal boundary segmentation in OCT images as they do not properly encode anatomical priors such as layer ordering, allowing segmented layer to occur at unreasonable locations [15].”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [15]
Anatomically coherent layer ordering is guaranteed by the method as in [15].
“Anatomically coherent layer ordering is guaranteed as in [15]:”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [15]
Except for the method from He et al. [15], all compared methods were trained using the available source code.
“Except for[15], for which the results are directly retrieved from the publication, all methods were trained having as base the available source code.”
From Interactive Deep Learning-Based Retinal OCT Layer Segmentation Refinement by Regressing Translation Maps (2024) · cites this paper as [15]
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Cites
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Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Topology Aware Fully Convolutional Networks for Histology Gland Segmentation
BenTaieb, A., & Hamarneh, G. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46723-8_53 · Cited by 163 · see citing works
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Cited by
Sorted by citations
Aresta, G., Araújo, T., Fazekas, B., Mai, J., Schmidt-Erfurth, U., & Bogunović, H. (2024) · IEEE Access · doi:10.1109/access.2024.3379015 · Cited by 5 · see citing works
“Recently, [15] proposed a deep model that relies on both layer and boundary segmentations while imposing anatomical constraints, achieving low error without additional post-processing.”
BackgroundMethodInfluential
- Authors
- He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L.
- Affiliations
- Johns Hopkins University (US)
- Portland State University (US)
- Corresponding author
- He, Yufan
- ORCID
- Venue
- Medical Image Analysis
- Publisher
- Elsevier BV
- Published
- 2021-02
- Volume
- 68
- Pages
- 101856
- ISSN
- 1361-8415 (ISSN-L)
- PMC
- PMC7855873
- OpenAlex
- W3092762369
- Cited by
- 104
- References
- 2
- Citations by year
- 2026: 5 · 2025: 25 · 2024: 25 · 2023: 26 · 2022: 17 · 2021: 6
- FWCI
- 5.83
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Medical Image Analysis (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- PDXScholar (Portland State University) (submittedVersion)
- Funders
- National Institute of Neurological Disorders and Stroke
- National Eye Institute
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- retinal layer segmentation, optical coherence tomography, retinal thickness, multiple sclerosis, diabetic macular edema, automated retinal analysis, sub-pixel accuracy
- MeSH terms
- Algorithms, Diabetic Retinopathy, Humans, Macular Edema, Retina, Tomography, Optical Coherence
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