FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading

Or, Abramovich,; Pizem, Hadas; Eijgen, Jan Van; Oren, Ilan; Melamed, Joshua; Stalmans, Ingeborg; Blumenthal, Eytan Z.; Behar, Joachim A.

doi:10.1016/j.cmpb.2023.107522

Cited by 17 publications

(3 citation statements)

References 32 publications

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“…Recent work in automated fundus has also reported high performance in distinguishing low and high-quality fundus images, with AUROCs of 0.95 to 0.99. 14 – 17 Our AUROC on the OHTS data is within this range. One important distinction to note, however, is that our ground truth is explicitly based on gradeability for a particular disease of interest rather than general image quality, which is the typical approach taken in previous work.…”

Section: Discussionmentioning

confidence: 56%

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Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection

Chuter,

Huynh,

Bowd

et al. 2024

Trans. Vis. Sci. Tech.

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Purpose To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater ( P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80–0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67–0.88). Conclusions The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.

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Section: Discussionmentioning

confidence: 56%

“…Previous work has described automated approaches for fundus image quality assessment. 14 – 17 These approaches have achieved high accuracy in identifying images with serious quality issues. However, these approaches are typically designed to address general image quality issues in a disease-agnostic way.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection

Chuter,

Huynh,

Bowd

et al. 2024

Trans. Vis. Sci. Tech.

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“…Generally, most methods use convolutional neural networks. For example, Abramovich et al [39] proposed the Fundus-Net model to grade the severity of lesions in fundus images on a range of 1 to 10 and achieved satisfactory results. A deep neural network was developed [40] to classify sagittal lumbar spine MRI images based on the severity of intervertebral disc lesions.…”

Section: Grading Diagnosismentioning

confidence: 99%

Multitask Learning for Concurrent Grading Diagnosis and Semi-Supervised Segmentation of Honeycomb Lung in CT Images

Dong,

Yang,

Feng

2024

Electronics

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Honeycomb lung is a radiological manifestation of various lung diseases, seriously threatening patients’ lives worldwide. In clinical practice, the precise localization of lesions and assessment of their severity are crucial. However, accurate segmentation and grading are challenging for physicians due to the heavy annotation burden and diversity of honeycomb lungs. In this paper, we propose a multitask learning architecture for semi-supervised segmentation and grading diagnosis to achieve automatic localization and assessment of lesions. To the best of our knowledge, this is the first method that integrates a grading diagnosis task into honeycomb lung semi-supervised segmentation. Firstly, we adapt cross-learning to capture local features and long-range dependencies from the CNN and transformer. Secondly, considering the diversity of honeycomb lung lesions, the shape-edge aware constraint is designed to assist the model in locating lesions. Then, in order to better understand the different levels of information in the images, we develop global contrast and local contrast learning to enhance the model’s learning of semantic-level and pixel-level features. Lastly, aiming to improve the diagnostic accuracy, we propose a gradient thresholding algorithm to integrate the segmentation predictions into the grading diagnosis network. The experiment’s results based on the in-house honeycomb lung dataset demonstrate the superiority of our method. Compared to other methods, our approach achieves a state-of-the-art performance. In particular, in external data testing, our predictions are consistent with physicians in the majority of cases. In addition, the segmentation results based on the public Kvasir-SEG dataset also indicate that our method has good generalization ability.

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PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation

Fhima

Eijgen

Stalmans

et al. 2023

Lecture Notes in Computer Science

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The retina is the only part of the human body in which blood vessels can be accessed non-invasively using imaging techniques such as digital fundus images (DFI). The spatial distribution of the retinal microvasculature may change with cardiovascular diseases and thus the eyes may be regarded as a window to our hearts. Computerized segmentation of the retinal arterioles and venules (A/V) is essential for automated microvasculature analysis. Using active learning, we created a new DFI dataset containing 240 crowd-sourced manual A/V segmentations performed by fifteen medical students and reviewed by an ophthalmologist, and developed LUNet, a novel deep learning architecture for high resolution A/V segmentation. LUNet architecture includes a double dilated convolutional block that aims to enhance the receptive field of the model and reduce its parameter count. Furthermore, LUNet has a long tail that operates at high resolution to refine the segmentation. The custom loss function emphasizes the continuity of the blood vessels. LUNet is shown to significantly outperform two state-of-the-art segmentation algorithms on the local test set as well as on four external test sets simulating distribution shifts across ethnicity, comorbidities, and annotators. We make the newly created dataset open access (upon publication).

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FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading

Cited by 17 publications

References 32 publications

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection

Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection

Multitask Learning for Concurrent Grading Diagnosis and Semi-Supervised Segmentation of Honeycomb Lung in CT Images

PVBM: A Python Vasculature Biomarker Toolbox Based on Retinal Blood Vessel Segmentation

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