Ensembling U-Nets for microaneurysm segmentation in optical coherence tomography angiography in patients with diabetic retinopathy

Sadda, S. R. et al. Quantitative assessment of the severity of diabetic retinopathy. Am. J. Ophthalmol. 218, 342–352. https://doi.org/10.1016/j.ajo.2020.05.021 (2020).Article 
PubMed 

Google Scholar 
Choi, W. et al. Ultrahigh speed swept source optical coherence tomography angiography of retinal and choriocapillaris alterations in diabetic patients with and without retinopathy. Retina 37, 11–21. https://doi.org/10.1097/IAE.0000000000001250 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
de Carlo, T. E. et al. Detection of microvascular changes in eyes of patients with diabetes but not clinical diabetic retinopathy using optical coherence tomography angiography. Retina 35, 2364–2370. https://doi.org/10.1097/IAE.0000000000000882 (2015).Article 
PubMed 

Google Scholar 
Querques, G., Borrelli, E., Battista, M., Sacconi, R. & Bandello, F. Optical coherence tomography angiography in diabetes: Focus on microaneurysms. Eye https://doi.org/10.1038/s41433-020-01173-7 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Couturier, A. et al. Capillary plexus anomalies in diabetic retinopathy on optical coherence tomography angiography. Retina 35, 2384–2391. https://doi.org/10.1097/IAE.0000000000000859 (2015).Article 
PubMed 

Google Scholar 
Husvogt, L., Ploner, S. & Maier, A. Optical coherence tomography. In Medical Imaging Systems (eds Maier, A. et al.), chap. 12, 251–261, https://doi.org/10.1007/978-3-319-96520-8_12 (Springer, 2018).Seebock, P. et al. Unsupervised identification of disease marker candidates in retinal OCT imaging data. IEEE Trans. Med. Imaging 38, 1037–1047. https://doi.org/10.1109/TMI.2018.2877080 (2018).Article 
PubMed 

Google Scholar 
De Fauw, J. et al. Clinically applicable deep learning for diagnosis and referral in retinal disease. Nat. Med. https://doi.org/10.1038/s41591-018-0107-6 (2018).Article 
PubMed 

Google Scholar 
Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (eds Navab, N. et al.), Vol. 9351, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28 (Springer, 2015).Inoue, T., Hatanaka, Y., Okumura, S., Muramatsu, C. & Fujita, H. Automated microaneurysm detection method based on eigenvalue analysis using hessian matrix in retinal fundus images. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 5873–5876, https://doi.org/10.1109/EMBC.2013.6610888 (IEEE, 2013).Pallawala, P. M., Hsu, W., Lee, M. L. & Goh, S. S. Automated microaneurysm segmentation and detection using generalized eigenvectors. In Proceedings – Seventh IEEE Workshop on Applications of Computer Vision, WACV, Vol. 322–327. https://doi.org/10.1109/ACVMOT.2005.26 (IEEE Computer Society, 2005).Giancardo, L. et al. Microaneurysm detection with radon transform-based classification on retina images. In Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 5939–5942, https://doi.org/10.1109/IEMBS.2011.6091562 (2011).Pereira, C. et al. Using a multi-agent system approach for microaneurysm detection in fundus images. Artif. Intell. Med. 60, 179–188. https://doi.org/10.1016/j.artmed.2013.12.005 (2014).Article 
PubMed 

Google Scholar 
Javidi, M., Pourreza, H. R. & Harati, A. Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation. Comput. Methods Programs Biomed. 139, 93–108. https://doi.org/10.1016/j.cmpb.2016.10.015 (2017).Article 
PubMed 

Google Scholar 
Dai, L. et al. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nat. Commun. 12, 1–11. https://doi.org/10.1038/s41467-021-23458-5 (2021).Article 
ADS 

Google Scholar 
Wang, Z., Chen, K.-J. & Zhang, L. A R-CNN based approach for microaneurysm detection in retinal fundus images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11837 LNCS, 201–212, https://doi.org/10.1007/978-3-030-32962-4_19 (2019).Feng, Z. et al. Deep retinal image segmentation: A FCN-based architecture with short and long skip connections for retinal image segmentation. In Neural Information Processing, Vol. 10637 LNCS, 713–722, https://doi.org/10.1007/978-3-319-70093-9_76 (Springer Verlag, 2017).Qiao, L., Zhu, Y. & Zhou, H. Diabetic retinopathy detection using prognosis of microaneurysm and early diagnosis system for non-proliferative diabetic retinopathy based on deep learning algorithms. IEEE Access 8, 104292–104302. https://doi.org/10.1109/ACCESS.2020.2993937 (2020).Article 

Google Scholar 
Xu, Y. et al. FFU-Net: Feature fusion U-Net for lesion segmentation of diabetic retinopathy. BioMed Res. Int. https://doi.org/10.1155/2021/6644071 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Tan, J. H. et al. Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 420, 66–76. https://doi.org/10.1016/j.ins.2017.08.050 (2017).Article 

Google Scholar 
Spencer, T., Olson, J. A., McHardy, K. C., Sharp, P. F. & Forrester, J. V. An image-processing strategy for the segmentation and quantification of microaneurysms in fluorescein angiograms of the ocular fundus. Comput. Biomed. Res. 29, 284–302. https://doi.org/10.1006/cbmr.1996.0021 (1996).Article 
PubMed 

Google Scholar 
Mendonca, A. M., Campilho, A. J. & Nunes, J. M. Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients. In Proceedings – International Conference on Image Analysis and Processing, ICIAP, Vol. 728–733. https://doi.org/10.1109/ICIAP.1999.797681 (IEEE Computer Society, 1999).Bilal, A., Sun, G., Mazhar, S., Imran, A. & Latif, J. A Transfer Learning and U-Net-based automatic detection of diabetic retinopathy from fundus images. Comput. Methods Biomech. Biomed. Eng. Imaging Vis. https://doi.org/10.1080/21681163.2021.2021111 (2022).Article 

Google Scholar 
Kou, C., Li, W., Liang, W., Yu, Z. & Hao, J. Microaneurysms segmentation with a U-Net based on recurrent residual convolutional neural network. J. Med. Imaging 6, 1. https://doi.org/10.1117/1.JMI.6.2.025008 (2019).Article 

Google Scholar 
Sambyal, N., Saini, P., Syal, R. & Gupta, V. Modified U-Net architecture for semantic segmentation of diabetic retinopathy images. Biocybern. Biomed. Eng. 40, 1094–1109. https://doi.org/10.1016/j.bbe.2020.05.006 (2020).Article 

Google Scholar 
Andersen, J. K. H., Grauslund, J. & Savarimuthu, T. R. Comparing objective functions for segmentation and detection of microaneurysms in retinal images. In Proceedings of Machine Learning Research, Vol. 121, 19–32 (PMLR, 2020).Perdomo, O. et al. Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography. Comput. Methods Programs Biomed. 178, 181–189. https://doi.org/10.1016/j.cmpb.2019.06.016 (2019).Article 
PubMed 

Google Scholar 
Husvogt, L. et al. Automatic detection of capillary dilation and looping in patients with diabetic retinopathy from optical coherence tomography angiography data. In Investigative Ophthalmology & Visual Science, Vol. 59, 5380 (C.V. Mosby Co, 2018).Husvogt, L. et al. First approaches towards automatic detection of microaneurysms in OCTA images. In Informatik aktuell, (eds Maier, A. et al.) Vol. 211279, 11–12, https://doi.org/10.1007/978-3-662-56537-7_11 (Springer Vieweg, 2018).Le, D., Alam, M., Miao, B. A., Lim, J. I. & Yao, X. Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy. Biomed. Opt. Express 10, 2493. https://doi.org/10.1364/BOE.10.002493 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Takase, N. et al. Enlargement of foveal avascular zone in diabetic eyes evaluated by en face optical coherence tomography angiography. Retina 35, 2377–2383. https://doi.org/10.1097/IAE.0000000000000849 (2015).Article 
PubMed 

Google Scholar 
Gao, W. et al. Detection of diabetic retinopathy in its early stages using textural features of optical coherence tomography angiography. J. Innov. Opt. Health Sci. 15, 2250006. https://doi.org/10.1142/S1793545822500067/ASSET/IMAGES/LARGE/S1793545822500067FIGF1.JPEG (2022).Article 

Google Scholar 
Le, D. et al. Transfer learning for automated octa detection of diabetic retinopathy. Transl. Vis. Sci. Technol. 9, 1–9. https://doi.org/10.1167/tvst.9.2.35 (2020).Article 

Google Scholar 
Heisler, M. et al. Ensemble deep learning for diabetic retinopathy detection using optical coherence tomography angiography. Transl. Vis. Sci. Technol. 9, 20. https://doi.org/10.1167/tvst.9.2.20 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Eladawi, N. et al. Early diabetic retinopathy diagnosis based on local retinal blood vessel analysis in optical coherence tomography angiography (OCTA) images. Med. Phys. 45, 4582–4599. https://doi.org/10.1002/mp.13142 (2018).Article 
PubMed 

Google Scholar 
Eladawi, N. et al. Early signs detection of diabetic retinopathy using optical coherence tomography angiography scans based on 3D multi-path convolutional neural network. In 2019 IEEE International Conference on Image Processing (ICIP), 1390–1394, https://doi.org/10.1109/ICIP.2019.8803031 (IEEE, 2019).Ryu, G., Lee, K., Park, D., Park, S. H. & Sagong, M. A deep learning model for identifying diabetic retinopathy using optical coherence tomography angiography. Sci. Rep. 11, 1–9. https://doi.org/10.1038/s41598-021-02479-6 (2021).Article 

Google Scholar 
Watson, D. S. et al. Clinical applications of machine learning algorithms: Beyond the black box. BMJ https://doi.org/10.1136/bmj.l886 (2019).Article 
PubMed 

Google Scholar 
Petch, J., Di, S. & Nelson, W. Opening the black box: The promise and limitations of explainable machine learning in cardiology. Can. J. Cardiol. https://doi.org/10.1016/j.cjca.2021.09.004 (2022).Article 
PubMed 

Google Scholar 
Ratti, E. & Graves, M. Explainable machine learning practices: Opening another black box for reliable medical AI. AI Ethics 2, 801–814. https://doi.org/10.1007/s43681-022-00141-z (2022).Article 

Google Scholar 
Bertram, C. A., Aubreville, M., Marzahl, C., Maier, A. & Klopfleisch, R. A large-scale dataset for mitotic figure assessment on whole slide images of canine cutaneous mast cell tumor. Sci. Data https://doi.org/10.1038/s41597-019-0290-4 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Marzahl, C. et al. EXACT: a collaboration toolset for algorithm-aided annotation of images with annotation version control. Sci. Rep. https://doi.org/10.1038/s41598-021-83827-4 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
Hasegawa, N., Nozaki, M., Takase, N., Yoshida, M. & Ogura, Y. New insights into microaneurysms in the deep capillary plexus detected by optical coherence tomography angiography in diabetic macular edema. Investig. Ophthalmol. Vis. Sci. 57, OCT348–OCT355. https://doi.org/10.1167/iovs.15-18782 (2016).Article 

Google Scholar 
Drozdzal, M., Vorontsov, E., Chartrand, G., Kadoury, S. & Pal, C. The importance of skip connections in biomedical image segmentation. In Deep Learning and Data Labeling for Medical Applications (eds Carneiro, G. et al.), Vol. 10008, 179–187, https://doi.org/10.1007/978-3-319-46976-8_19 (Springer, 2016).Lin, T. Y., Goyal, P., Girshick, R., He, K. & Dollár, P. Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017).Chen, J. et al. TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv https://doi.org/10.48550/arXiv.2102.04306 (2021).Cao, H. et al. Swin-unet: Unet-like pure transformer for medical image segmentation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13803 LNCS, 205–218, https://doi.org/10.1007/978-3-031-25066-8_9 (2023).Nam, K. Y., Lee, M. W., Lee, K. H. & Kim, J. Y. Superficial capillary plexus vessel density/deep capillary plexus vessel density ratio in healthy eyes. BMC Ophthalmol. https://doi.org/10.1186/s12886-022-02673-8 (2022).Article 
PubMed 
PubMed Central 

Google Scholar