ArticleBiomedical optics express2019
Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT.
Article in Biomedical optics express, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 63 citations in OpenAlex.
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Deep learning in imaging analysis for multiple sclerosis: Diagnosis and monitoring.Caspian journal of internal medicine · 2026Review
- Structural-prior guided and feature-enhanced transformer with masked image modeling pretraining for retinal layers and fluid segmentation in macular edema OCT images.Biomedical optics express · 2025Article
- Article
- Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.Journal of imaging informatics in medicine · 2024Article
- ScLNet: A cornea with scleral lens OCT layers segmentation dataset and new multi-task model.Heliyon · 2024Article
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Longitudinal deep network for consistent OCT layer segmentation.Biomedical optics express · 2023Article
- Retinal OCT Layer Segmentation via Joint Motion Correction and Graph-Assisted 3D Neural Network.IEEE access : practical innovations, open solutions · 2023Article
- Intraretinal Layer Segmentation Using Cascaded Compressed U-Nets.Journal of imaging · 2022Article
- OCT Retinal and Choroidal Layer Instance Segmentation Using Mask R-CNN.Sensors (Basel, Switzerland) · 2022Article
- Early Diagnosis of Multiple Sclerosis Using Swept-Source Optical Coherence Tomography and Convolutional Neural Networks Trained with Data Augmentation.Sensors (Basel, Switzerland) · 2021Article
- Segmentation of Preretinal Space in Optical Coherence Tomography Images Using Deep Neural Networks.Sensors (Basel, Switzerland) · 2021Article
- Weakly supervised anomaly segmentation in retinal OCT images using an adversarial learning approach.Biomedical optics express · 2021Article
- Past, present and future role of retinal imaging in neurodegenerative disease.Progress in retinal and eye research · 2021Review
- Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images.Biomedical optics express · 2021Article
- Structured layer surface segmentation for retina OCT using fully convolutional regression networks.Medical image analysis · 2021Article
- Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning.Translational vision science & technology · 2020Article
- Approaches to quantify optical coherence tomography angiography metrics.Annals of translational medicine · 2020Review
- Semi-supervised deep learning based 3D analysis of the peripapillary region.Biomedical optics express · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.