MsTGANet: Automatic Drusen Segmentation From Retinal OCT Images

Wang, Meng; Zhu, Weifang; Shi, Fei; Su, Jinzhu; Chen, Haoyu; Yu, Kai; Zhou, Yi; Peng, Yuanyuan; Chen, Zhongyue; Chen, Xinjian

doi:10.1109/tmi.2021.3112716

Cited by 41 publications

(13 citation statements)

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“…In the procedure of the OCT scan, the average examination time was 18.4 s for one eye and the average output time of the AI report was 4 min. The high accuracy and efficiency were proposed due to 1) the strategy in architecture and model establishment, as well as the online hard example mining mechanism utilized to improve the convergence speed due to the unbalanced ratio between the foreground and background; 2) accumulated experience and advanced technology in OCT image analysis with AI algorithms ( Sun et al, 2016 ; Zhu et al, 2017 ; Shi et al, 2019 ; Shi et al, 2021 ; Wang et al, 2022 ); 3) the integrated design of the OCT instrument and AI algorithm; 4) the high performance of the OCT instrument with a maximum A scan speed at 45,000 times per second as well as the high resolution of the images. The aforementioned issues all guaranteed the accuracy and speed of the community screening work.…”

Section: Discussionmentioning

confidence: 99%

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Bai

Wan

et al. 2022

Front. Cell Dev. Biol.

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Objective: To evaluate the accuracy and feasibility of the auto-detection of 15 retinal disorders with artificial intelligence (AI)-assisted optical coherence tomography (OCT) in community screening.Methods: A total of 954 eyes of 477 subjects from four local communities were enrolled in this study from September to December 2021. They received OCT scans covering an area of 12 mm × 9 mm at the posterior pole retina involving the macular and optic disc, as well as other ophthalmic examinations performed using their demographic information recorded. The OCT images were analyzed using integrated software with the previously established algorithm based on the deep-learning method and trained to detect 15 kinds of retinal disorders, namely, pigment epithelial detachment (PED), posterior vitreous detachment (PVD), epiretinal membranes (ERMs), sub-retinal fluid (SRF), choroidal neovascularization (CNV), drusen, retinoschisis, cystoid macular edema (CME), exudation, macular hole (MH), retinal detachment (RD), ellipsoid zone disruption, focal choroidal excavation (FCE), choroid atrophy, and retinal hemorrhage. Meanwhile, the diagnosis was also generated from three groups of individual ophthalmologists (group of retina specialists, senior ophthalmologists, and junior ophthalmologists) and compared with those by the AI. The area under the receiver operating characteristic curve (AUC), sensitivity, and specificity were calculated, and kappa statistics were performed.Results: A total of 878 eyes were finally enrolled, with 76 excluded due to poor image quality. In the detection of 15 retinal disorders, the ROC curve comparison between AI and professors’ presented relatively large AUC (0.891–0.997), high sensitivity (87.65–100%), and high specificity (80.12–99.41%). Among the ROC curve comparisons with those by the retina specialists, AI was the closest one to the professors’ compared to senior and junior ophthalmologists (p < 0.05).Conclusion: AI-assisted OCT is highly accurate, sensitive, and specific in auto-detection of 15 kinds of retinal disorders, certifying its feasibility and effectiveness in community ophthalmic screening.

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Section: Discussionmentioning

confidence: 99%

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Bai

Wan

et al. 2022

Front. Cell Dev. Biol.

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“…For these challenges, Aladhadh et al 118 proposed a medical vision transformer (MVT), a two‐stage framework designed to introduce the attention mechanism for skin cancer classification. Nakai et al 119 proposed a novel bottleneck Transformer network (DPE‐BoTNeT) by joining convolution network with the Transformer design to supplement the initial network with the capability of extracting global dependency and interpretating the positional information.…”

Section: Medical Image Classificationmentioning

confidence: 99%

Recent advances of Transformers in medical image analysis: A comprehensive review

Xia

Wang

2023

MedComm – Future Medicine

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Recent works have shown that Transformer's excellent performances on natural language processing tasks can be maintained on natural image analysis tasks. However, the complicated clinical settings in medical image analysis and varied disease properties bring new challenges for the use of Transformer. The computer vision and medical engineering communities have devoted significant effort to medical image analysis research based on Transformer with especial focus on scenario-specific architectural variations.In this paper, we comprehensively review this rapidly developing area by covering the latest advances of Transformer-based methods in medical image analysis of different settings. We first give introduction of basic mechanisms of Transformer including implementations of selfattention and typical architectures. The important research problems in various medical image data modalities, clinical visual tasks, organs and diseases are then reviewed systemically. We carefully collect 276 very recent works and 76 public medical image analysis datasets in an organized structure. Finally, discussions on open problems and future research directions are also provided. We expect this review to be an up-to-date roadmap and serve as a reference source in pursuit of boosting the development of medical image analysis field.

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“…Most of them approach the problem as a semantic segmentation problem where segmentation masks of the same size as the input image are predicted. Existing models differ in architecture or loss functions and focus on the segmentation of different biomarkers like drusen 4 – 7 , hyperreflective foci (HRF) 8 , 9 , or fluid in or below the retina 10 . Treating the localization of retinal layers in OCT as a semantic segmentation problem is attractive due to the maturity and ease of use of corresponding neural network architectures.…”

Section: Introductionmentioning

confidence: 99%

Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights

Morelle

Wintergerst

Finger

et al. 2023

Sci Rep

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Drusen are an important biomarker for age-related macular degeneration (AMD). Their accurate segmentation based on optical coherence tomography (OCT) is therefore relevant to the detection, staging, and treatment of disease. Since manual OCT segmentation is resource-consuming and has low reproducibility, automatic techniques are required. In this work, we introduce a novel deep learning based architecture that directly predicts the position of layers in OCT and guarantees their correct order, achieving state-of-the-art results for retinal layer segmentation. In particular, the average absolute distance between our model’s prediction and the ground truth layer segmentation in an AMD dataset is 0.63, 0.85, and 0.44 pixel for Bruch's membrane (BM), retinal pigment epithelium (RPE) and ellipsoid zone (EZ), respectively. Based on layer positions, we further quantify drusen load with excellent accuracy, achieving 0.994 and 0.988 Pearson correlation between drusen volumes estimated by our method and two human readers, and increasing the Dice score to 0.71 ± 0.16 (from 0.60 ± 0.23) and 0.62 ± 0.23 (from 0.53 ± 0.25), respectively, compared to a previous state-of-the-art method. Given its reproducible, accurate, and scalable results, our method can be used for the large-scale analysis of OCT data.

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MsTGANet: Automatic Drusen Segmentation From Retinal OCT Images

Cited by 41 publications

References 50 publications

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Accuracy and feasibility with AI-assisted OCT in retinal disorder community screening

Recent advances of Transformers in medical image analysis: A comprehensive review

Accurate drusen segmentation in optical coherence tomography via order-constrained regression of retinal layer heights

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