Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-

Segmentation of optic disc in retinal images for glaucoma diagnosis by saliency level set with enhanced active contour model

Glaucoma is an ophthalmic disease which is among the chief causes of visual impairment across the globe. The clarity of the optic disc (OD) is crucial for recognizing glaucoma. Since existing methods are unable to successfully integrate multi-view information derived from shape and appearance to precisely explain OD for segmentation, this paper proposes a saliency-based level set with an enhanced active contour method (SL-EACM), a modified locally statistical active contour model, and entropy-based optical disc localization. The significant contributions are that i) the SL-EACM is introduced to address the often noticed problem of intensity inhomogeneity brought on by defects in imaging equipment or fluctuations in lighting; ii) to prevent the integrity of the OD structures from being compromised by pathological alterations and artery blockage, local image probability data is included from a multi-dimensional feature space around the region of interest in the model; and iii) the model incorporates prior shape information into the technique, for enhancing the accuracy in identifying the OD structures from surrounding regions. Public databases such as CHASE_DB, DRIONS-DB, and Drishti-GS are used to evaluate the proposed model. The findings from numerous trials demonstrate that the proposed model outperforms state-of-the-art approaches in terms of qualitative and quantitative outcomes

PAPILA: Dataset with fundus images and clinical data of both eyes of the same patient for glaucoma assessment

Glaucoma is one of the ophthalmological diseases that frequently causes loss of vision in today’s society. Previous studies assess which anatomical parameters of the optic nerve can be predictive of glaucomatous damage, but to date there is no test that by itself has sufficient sensitivity and specificity to diagnose this disease. This work provides a public dataset with medical data and fundus images of both eyes of the same patient. Segmentations of the cup and optic disc, as well as the labeling of the patients based on the evaluation of clinical data are also provided. The dataset has been tested with a neural network to classify healthy and glaucoma patients. Specifically, the ResNet-50 has been used as the basis to classify patients using information from each eye independently as well as using the joint information from both eyes of each patient. Results provide the baseline metrics, with the aim of promoting research in the early detection of glaucoma based on the joint analysis of both eyes of the same patient.This work has been partially funded by Spanish National projects AES2017-PI17/00771 and AES2017-PI17/00821 (Instituto de Salud Carlos III) and regional project 20901/PI/18 (Fundación Séneca)

Multi-modal classifier fusion with feature cooperation for glaucoma diagnosis

Background: Glaucoma is a major public health problem that can lead to an optic nerve lesion, requiring systematic screening in the population over 45 years of age. The diagnosis and classification of this disease have had a marked and excellent development in recent years, particularly in the machine learning domain. Multimodal data have been shown to be a significant aid to the machine learning domain, especially by its contribution to improving data driven decision-making. Method: Solving classification problems by combinations of classifiers has made it possible to increase the robustness as well as the classification reliability by using the complementarity that may exist between the classifiers. Complementarity is considered a key property of multimodality. A Convolutional Neural Network (CNN) works very well in pattern recognition and has been shown to exhibit superior performance, especially for image classification which can learn by themselves useful features from raw data. This article proposes a multimodal classification approach based on deep Convolutional Neural Network and Support Vector Machine (SVM) classifiers using multimodal data and multimodal feature for glaucoma diagnosis from retinal fundus images from RIM-ONE dataset. We make use of handcrafted feature descriptors such as the Gray Level Co-Occurrence Matrix, Central Moments and Hu Moments to co-operate with features automatically generated by the CNN in order to properly detect the optic nerve and consequently obtain a better classification rate, allowing a more reliable diagnosis of glaucoma. Results: The experimental results confirm that the combination of classifiers using the BWWV technique is better than learning classifiers separately. The proposed method provides a computerized diagnosis system for glaucoma disease with impressive results comparing them to the main related studies that allow us to continue in this research path

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review.

Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma, examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is non-invasive and low-cost; however, the image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?". Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011-2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modelling based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research

Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images

[EN] Estimated blind people in the world will exceed 40 million by 2025. To develop novel algorithms based on fundus image descriptors that allow the automatic classification of retinal tissue into healthy and pathological in early stages is necessary. In this paper, we focus on one of the most common pathologies in the current society: diabetic retinopathy. The proposed method avoids the necessity of lesion segmentation or candidate map generation before the classification stage. Local binary patterns and granulometric profiles are locally computed to extract texture and morphological information from retinal images. Different combinations of this information feed classification algorithms to optimally discriminate bright and dark lesions from healthy tissues. Through several experiments, the ability of the proposed system to identify diabetic retinopathy signs is validated using different public databases with a large degree of variability and without image exclusion.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness through project DPI2016-77869 and GVA through project PROMETEO/2019/109Colomer, A.; Igual García, J.; Naranjo Ornedo, V. (2020). Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images. Sensors. 20(4):1-20. https://doi.org/10.3390/s20041005S120204World Report on Vision. Technical Report, 2019https://www.who.int/publications-detail/world-report-on-visionFong, D. S., Aiello, L., Gardner, T. W., King, G. L., Blankenship, G., Cavallerano, J. D., … Klein, R. (2003). Retinopathy in Diabetes. Diabetes Care, 27(Supplement 1), S84-S87. doi:10.2337/diacare.27.2007.s84COGAN, D. G. (1961). Retinal Vascular Patterns. Archives of Ophthalmology, 66(3), 366. doi:10.1001/archopht.1961.00960010368014Wilkinson, C. ., Ferris, F. L., Klein, R. E., Lee, P. P., Agardh, C. D., Davis, M., … Verdaguer, J. T. (2003). Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology, 110(9), 1677-1682. doi:10.1016/s0161-6420(03)00475-5Universal Eye Health: A Global Action Plan 2014–2019. Technical Reporthttps://www.who.int/blindness/actionplan/en/Salamat, N., Missen, M. M. S., & Rashid, A. (2019). Diabetic retinopathy techniques in retinal images: A review. Artificial Intelligence in Medicine, 97, 168-188. doi:10.1016/j.artmed.2018.10.009Qureshi, I., Ma, J., & Shaheed, K. (2019). A Hybrid Proposed Fundus Image Enhancement Framework for Diabetic Retinopathy. Algorithms, 12(1), 14. doi:10.3390/a12010014Morales, S., Engan, K., Naranjo, V., & Colomer, A. (2017). Retinal Disease Screening Through Local Binary Patterns. IEEE Journal of Biomedical and Health Informatics, 21(1), 184-192. doi:10.1109/jbhi.2015.2490798Asiri, N., Hussain, M., Al Adel, F., & Alzaidi, N. (2019). Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artificial Intelligence in Medicine, 99, 101701. doi:10.1016/j.artmed.2019.07.009Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., … Webster, D. R. (2016). Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22), 2402. doi:10.1001/jama.2016.17216Prentašić, P., & Lončarić, S. (2016). Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Computer Methods and Programs in Biomedicine, 137, 281-292. doi:10.1016/j.cmpb.2016.09.018Costa, P., Galdran, A., Meyer, M. I., Niemeijer, M., Abramoff, M., Mendonca, A. M., & Campilho, A. (2018). End-to-End Adversarial Retinal Image Synthesis. IEEE Transactions on Medical Imaging, 37(3), 781-791. doi:10.1109/tmi.2017.2759102De la Torre, J., Valls, A., & Puig, D. (2020). A deep learning interpretable classifier for diabetic retinopathy disease grading. Neurocomputing, 396, 465-476. doi:10.1016/j.neucom.2018.07.102Diaz-Pinto, A., Colomer, A., Naranjo, V., Morales, S., Xu, Y., & Frangi, A. F. (2019). Retinal Image Synthesis and Semi-Supervised Learning for Glaucoma Assessment. IEEE Transactions on Medical Imaging, 38(9), 2211-2218. doi:10.1109/tmi.2019.2903434Walter, T., Klein, J., Massin, P., & Erginay, A. (2002). A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina. IEEE Transactions on Medical Imaging, 21(10), 1236-1243. doi:10.1109/tmi.2002.806290Welfer, D., Scharcanski, J., & Marinho, D. R. (2010). A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Computerized Medical Imaging and Graphics, 34(3), 228-235. doi:10.1016/j.compmedimag.2009.10.001Mookiah, M. R. K., Acharya, U. R., Martis, R. J., Chua, C. K., Lim, C. M., Ng, E. Y. K., & Laude, A. (2013). Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach. Knowledge-Based Systems, 39, 9-22. doi:10.1016/j.knosys.2012.09.008Zhang, X., Thibault, G., Decencière, E., Marcotegui, B., Laÿ, B., Danno, R., … Erginay, A. (2014). Exudate detection in color retinal images for mass screening of diabetic retinopathy. Medical Image Analysis, 18(7), 1026-1043. doi:10.1016/j.media.2014.05.004Sopharak, A., Uyyanonvara, B., Barman, S., & Williamson, T. H. (2008). Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods. Computerized Medical Imaging and Graphics, 32(8), 720-727. doi:10.1016/j.compmedimag.2008.08.009Giancardo, L., Meriaudeau, F., Karnowski, T. P., Li, Y., Garg, S., Tobin, K. W., & Chaum, E. (2012). Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Medical Image Analysis, 16(1), 216-226. doi:10.1016/j.media.2011.07.004Amel, F., Mohammed, M., & Abdelhafid, B. (2012). Improvement of the Hard Exudates Detection Method Used For Computer- Aided Diagnosis of Diabetic Retinopathy. International Journal of Image, Graphics and Signal Processing, 4(4), 19-27. doi:10.5815/ijigsp.2012.04.03Usman Akram, M., Khalid, S., Tariq, A., Khan, S. A., & Azam, F. (2014). Detection and classification of retinal lesions for grading of diabetic retinopathy. Computers in Biology and Medicine, 45, 161-171. doi:10.1016/j.compbiomed.2013.11.014Akram, M. U., Tariq, A., Khan, S. A., & Javed, M. Y. (2014). Automated detection of exudates and macula for grading of diabetic macular edema. Computer Methods and Programs in Biomedicine, 114(2), 141-152. doi:10.1016/j.cmpb.2014.01.010Quellec, G., Lamard, M., Abràmoff, M. D., Decencière, E., Lay, B., Erginay, A., … Cazuguel, G. (2012). A multiple-instance learning framework for diabetic retinopathy screening. Medical Image Analysis, 16(6), 1228-1240. doi:10.1016/j.media.2012.06.003Decencière, E., Cazuguel, G., Zhang, X., Thibault, G., Klein, J.-C., Meyer, F., … Chabouis, A. (2013). TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM, 34(2), 196-203. doi:10.1016/j.irbm.2013.01.010Abràmoff, M. D., Folk, J. C., Han, D. P., Walker, J. D., Williams, D. F., Russell, S. R., … Niemeijer, M. (2013). Automated Analysis of Retinal Images for Detection of Referable Diabetic Retinopathy. JAMA Ophthalmology, 131(3), 351. doi:10.1001/jamaophthalmol.2013.1743Almotiri, J., Elleithy, K., & Elleithy, A. (2018). Retinal Vessels Segmentation Techniques and Algorithms: A Survey. Applied Sciences, 8(2), 155. doi:10.3390/app8020155Thakur, N., & Juneja, M. (2018). Survey on segmentation and classification approaches of optic cup and optic disc for diagnosis of glaucoma. Biomedical Signal Processing and Control, 42, 162-189. doi:10.1016/j.bspc.2018.01.014Bertalmio, M., Sapiro, G., Caselles, V., & Ballester, C. (2000). Image inpainting. Proceedings of the 27th annual conference on Computer graphics and interactive techniques - SIGGRAPH ’00. doi:10.1145/344779.344972Qureshi, M. A., Deriche, M., Beghdadi, A., & Amin, A. (2017). A critical survey of state-of-the-art image inpainting quality assessment metrics. Journal of Visual Communication and Image Representation, 49, 177-191. doi:10.1016/j.jvcir.2017.09.006Colomer, A., Naranjo, V., Engan, K., & Skretting, K. (2017). Assessment of sparse-based inpainting for retinal vessel removal. 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REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs

[EN] Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (https://refuge.grand-challenge.org), held in conjunction with MIC-CAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.This work was supported by the Christian Doppler Research Association, the Austrian Federal Ministry for Digital and Economic Affairs and the National Foundation for Research, Technology and Development, J.I.O is supported by WWTF (Medical University of Vienna: AugUniWien/FA7464A0249, University of Vienna: VRG12- 009). Team Masker is supported by Natural Science Foundation of Guangdong Province of China (Grant 2017A030310647). Team BUCT is partially supported by the National Natural Science Foundation of China (Grant 11571031). The authors would also like to thank REFUGE study group for collaborating with this challenge.Orlando, JI.; Fu, H.; Breda, JB.; Van Keer, K.; Bathula, DR.; Diaz-Pinto, A.; Fang, R.... (2020). REFUGE Challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs. Medical Image Analysis. 59:1-21. https://doi.org/10.1016/j.media.2019.101570S12159Abramoff, M. D., Garvin, M. K., & Sonka, M. (2010). Retinal Imaging and Image Analysis. IEEE Reviews in Biomedical Engineering, 3, 169-208. doi:10.1109/rbme.2010.2084567Abràmoff, M. D., Lavin, P. T., Birch, M., Shah, N., & Folk, J. C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine, 1(1). doi:10.1038/s41746-018-0040-6Al-Bander, B., Williams, B., Al-Nuaimy, W., Al-Taee, M., Pratt, H., & Zheng, Y. (2018). Dense Fully Convolutional Segmentation of the Optic Disc and Cup in Colour Fundus for Glaucoma Diagnosis. Symmetry, 10(4), 87. doi:10.3390/sym10040087Almazroa, A., Burman, R., Raahemifar, K., & Lakshminarayanan, V. (2015). Optic Disc and Optic Cup Segmentation Methodologies for Glaucoma Image Detection: A Survey. Journal of Ophthalmology, 2015, 1-28. doi:10.1155/2015/180972Burlina, P. M., Joshi, N., Pekala, M., Pacheco, K. D., Freund, D. E., & Bressler, N. M. (2017). Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks. JAMA Ophthalmology, 135(11), 1170. doi:10.1001/jamaophthalmol.2017.3782Carmona, E. J., Rincón, M., García-Feijoó, J., & Martínez-de-la-Casa, J. M. (2008). Identification of the optic nerve head with genetic algorithms. Artificial Intelligence in Medicine, 43(3), 243-259. doi:10.1016/j.artmed.2008.04.005Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16, 321-357. doi:10.1613/jair.953Christopher, M., Belghith, A., Bowd, C., Proudfoot, J. A., Goldbaum, M. H., Weinreb, R. N., … Zangwill, L. M. (2018). Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs. Scientific Reports, 8(1). doi:10.1038/s41598-018-35044-9De Fauw, J., Ledsam, J. R., Romera-Paredes, B., Nikolov, S., Tomasev, N., Blackwell, S., … Ronneberger, O. (2018). Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), 1342-1350. doi:10.1038/s41591-018-0107-6Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology, 33(3), 231. doi:10.5566/ias.1155DeLong, E. R., DeLong, D. M., & Clarke-Pearson, D. L. (1988). Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. 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Aspectos do rastreamento do glaucoma auxiliados por técnicas automatizadas em imagens com menor qualidade do disco óptico

O glaucoma é uma neuropatia óptica cuja progressão pode levar a cegueira. Representa a principal causa de perda visual de caráter irreversível em todo o mundo para homens e mulheres. A detecção precoce através de programas de rastreamento feita por especialistas é baseada nas características do nervo óptico, em biomarcadores oftalmológicos (destacando-se a pressão ocular) e exames subsidiários, com destaque ao campo visual e OCT. Após o reconhecimento dos casos é feito o tratamento com finalidade de estacionar a progressão da doença e melhorar a qualidade de vida dos pacientes. Contudo, estes programas têm limitações, principalmente em locais mais distantes dos grandes centros de tratamento especializado, insuficiência de equipamentos básicos e pessoal especializado para oferecer o rastreamento a toda a população, faltam meios para locomoção a estes centros, desinformação e desconhecimento da doença, além de características de progressão assintomática da doença. Esta tese aborda soluções inovadoras que podem contribuir para a automação do rastreamento do glaucoma utilizando aparelhos portáteis e mais baratos, considerando as necessidades reais dos clínicos durante o rastreamento. Para isso foram realizadas revisões sistemáticas sobre os métodos e equipamentos para apoio à triagem automática do glaucoma e os métodos de aprendizado profundo para a segmentação e classificação aplicáveis. Também foi feito um levantamento de questões médicas relativas à triagem do glaucoma e associá-las ao campo da inteligência artificial, para dar mais sentido as metodologias automatizadas. Além disso, foi criado um banco de dados privado, com vídeos e imagens de retina adquiridos por um smartphone acoplado a lente de baixo custo para o rastreamento do glaucoma e avaliado com métodos do estado da arte. Foram avaliados e analisados métodos de detecção automática de glaucoma utilizando métodos de aprendizado profundo de segmentação do disco e do copo óptico em banco de dados públicos de imagens de retina. Finalmente, foram avaliadas técnicas de mosaico e de detecção da cabeça do nervo óptico em imagens de baixa qualidade obtidas para pré-processamento de imagens adquiridas por smartphones acoplados a lente de baixo custo.Glaucoma is an optic neuropathy whose progression can lead to blindness. It represents the leading cause of irreversible visual loss worldwide for men and women. Early detection through screening programs carried out by specialists is based on the characteristics of the optic papilla, ophthalmic biomarkers (especially eye pressure), and subsidiary exams, emphasizing the visual field and optical coherence tomography (OCT). After recognizing the cases, the treatment is carried out to stop the progression of the disease and improve the quality of patients’ life. However, these screening programs have limitations, particularly in places further away from the sizeable, specialized treatment centers, due to the lack of essential equipment and technical personnel to offer screening to the entire population, due to the lack of means of transport to these centers, due to lack of information and lack of knowledge about the disease, considering the characteristics of asymptomatic progression of the disease. This thesis aims to develop innovative approaches to contribute to the automation of glaucoma screening using portable and cheaper devices, considering the real needs of clinicians during screening. For this, systematic reviews were carried out on the methods and equipment to support automatic glaucoma screening, and the applicable deep learning methods for segmentation and classification. A survey of medical issues related to glaucoma screening was carried out and associated with the field of artificial intelligence to make automated methodologies more effective. In addition, a private dataset was created, with videos and retina images acquired using a low-cost lens-coupled cell phone, for glaucoma screening and evaluated with state-of-the-art methods. Methods of automatic detection of glaucoma using deep learning methods of segmentation of the disc and optic cup were evaluated and analyzed in a public database of retinal images. In the case of deep learning classification methods, these were evaluated in public databases of retina images and in a private database with low-cost images. Finally, mosaic and object detection techniques were evaluated in low-quality images obtained for pre-processing images acquired by cell phones coupled with low-cost lenses