Paint by numbersSeptember 07, 2017
Researchers at the Helmholtz Zentrum München have developed a new method for reconstructing continuous biological processes, such as disease progression, using image data. The study was published in 'Nature Communications'.
Modern life sciences generate a constantly growing amount of data in shorter and shorter cycles. Making such data controllable and suitable for evaluation is the objective of Dr. Dr. Alexander Wolf and his colleagues at the Helmholtz Zentrum München's Institute of Computational Biology (ICB). With this in mind, the researchers are attempting to develop software that handles this evaluation. But of course there are various hurdles to clear.
"In the current study, we dealt with the problem that software cannot assign image data to continuous processes," explains study leader Wolf. "For example, it is possible to classify image information according to clearly defined categories, but in disease progression and developmental biology, the limits are quickly reached because the processes are continuous and not individual steps."
In order to take this into account, the Helmholtz team employed methods from so-called Deep Learning* (i.e. machine learning processes). "Using artificial neural networks, we can now combine individual pictures into processes and additionally display them in a way that humans understand," say Philipp Eulenberg and Niklas Köhler, former Master's students at the ICB and the study's first authors.
Blood cells and retinas as sparring partners
In order to demonstrate the method's capability, the scientists selected two examples. In the first approach, the software reconstructed the continuous cell cycle of white blood cells using images from an imaging flow cytometer (producing pictures in a fluorescence microscope). "A further advantage of this examination is that our software is so fast that it is possible to extract the cell development on the fly, meaning while the analysis in the cytometer is still running," explains Wolf. "In addition, our software makes six times less errors than previous approaches."
In the second experiment, the researchers reconstructed the progress of diabetic retinopathy.** "We did this by feeding our software 30,000 individual images of retinas as sparring partners, so to speak," explains Niklas Köhler. "Since it automatically compiles these data into a continuous process, the software allows us to predict the disease progression on a continuous scale."
And if the data are not part of a continuous biological process? "In such a case, the software recognizes that individual categories are involved and assigns the measured data to individual clusters," Wolf explains. In addition to further applications for the method, in the future Wolf and his colleagues want to solve other problems involving the evaluation of biological data using machine learning.
* Deep Learning algorithms simulate the learning processes in people using artificial neural networks. The principle functions particularly well when large quantities of data (Big Data) are available for training. Image recognition is one of Deep Learning's strengths. More decision layers are placed between the input and the output than usually found in neuronal networks, which is why the term "deep" is used.
** Diabetic retinopathy is the main cause of early vision loss in the Western world. The diagnosis is usually made by an expert, who assigns it to one of the four stages healthy, mild, medium and severe. Working with 8,000 images, the software was able to describe the progression or increasing severity of the disease without being provided with the ordering information.
Alex Wolf and the team recently took one of the top places in the Data Science Bowl, one of the world's highest endowed competitions in Big Data. For their entry, the team programmed an algorithm that recognizes lung cancer on the basis of 300 slices from a three-dimensional computer tomography scan in less than a few milliseconds, a process that can take a radiologist several hours in the worst case.
The ICB also deals with the subject of Deep Learning in other contents: The scientists recently introduced an algorithm in 'Nature Methods' that predicts hematopoietic stem cell development. In the video "Deep Learning Predicts Stem Cell Development", they explain how this works: https://www.youtube.com/watch?v=nZ46-fi8OF4&feature=youtu.be
Eulenberg, P. et al. (2017): Reconstructing cell cycle and disease progression using deep learning. Nature Communications, DOI: 10.1038/s41467-017-00623-3
The Helmholtz Zentrum München, the German Research Center for Environmental Health, pursues the goal of developing personalized medical approaches for the prevention and therapy of major common diseases such as diabetes and lung diseases. To achieve this, it investigates the interaction of genetics, environmental factors and lifestyle. The Helmholtz Zentrum München is headquartered in Neuherberg in the north of Munich and has about 2,300 staff members. It is a member of the Helmholtz Association, a community of 18 scientific-technical and medical-biological research centers with a total of about 37,000 staff members. http://www.helmholtz-muenchen.de/en
The Institute of Computational Biology (ICB) develops and applies methods for the model-based description of biological systems, using a data-driven approach by integrating information on multiple scales ranging from single-cell time series to large-scale omics. Given the fast technological advances in molecular biology, the aim is to provide and collaboratively apply innovative tools with experimental groups in order to jointly advance the understanding and treatment of common human diseases. http://www.helmholtz-muenchen.de/icb
Contact for the media:
Department of Communication, Helmholtz Zentrum München - German Research Center for Environmental Health, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 2238 - Fax: +49 89 3187 3324 - E-mail: email@example.com
Dr. Dr. Alexander Wolf, Helmholtz Zentrum München - German Research Center for Environmental Health, Institute of Computational Biology, Ingolstädter Landstr. 1, 85764 Neuherberg - Tel. +49 89 3187 4217, E-mail: firstname.lastname@example.org
Helmholtz Zentrum München - German Research Center for Environmental Health
Related Diabetic Retinopathy Articles:
Scientists at Joslin Diabetes Center have revealed an unexpected route to slow the progression of diabetic kidney disease, targeting a biological pathway that is the main channel for the metabolism of glucose in the cell.
Can a large-scale, primary care-based teleretinal diabetic retinopathy screening (TDRS) program reduce wait times for screening and improve the timeliness of care in the Los Angeles County Department of Health Services, the largest publicly operated county safety net health care system in the United States?
Many youths with type 1 and 2 diabetes are not receiving eye examinations as recommended to monitor for diabetic retinopathy, according to a study published online by JAMA Ophthalmology.
Mary Elizabeth Hartnett, M.D., and colleagues at the John A.
Researchers at Michigan Medicine led a group of internationally recognized endocrinologists and neurologists from both sides of the Atlantic and teamed up with the American Diabetes Association to craft a new position statement on the prevention, treatment and management of diabetic neuropathy.
A mini-symposium published in the Journal of the American Association for Pediatric Ophthalmology and Strabismus (AAPOS) provides important insights into new techniques and treatments that show promise for eliminating retinopathy of prematurity (ROP) throughout the world.
A more powerful version of an anti-inflammatory molecule already circulating in our blood may help protect our vision in the face of diabetes.
In an evaluation of retinal photographs from adults with diabetes, an algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy, according to a study published online by JAMA.
A study sheds new understanding on the mechanisms of the diabetic retinopathy -- which is the most prominent complication of diabetes and the leading cause of blindness in working age individuals -- as it uncovered a program of accelerated aging of the neurons, blood vessels and immune cells of the retina in areas where blood vessels had been damaged.
In middle-aged and older individuals with type 2 diabetes, intake of at least 500 mg/d of dietary long-chain ω-3 polyunsaturated fatty acids, easily achievable with 2 weekly servings of oily fish, was associated with a decreased risk of sight-threatening diabetic retinopathy, according to a study published online by JAMA Ophthalmology.
Related Diabetic Retinopathy Reading:
Diabetic Retinopathy (Contemporary Diabetes)
by Elia Duh (Editor)
Diabetic retinopathy is the most common microvascular complication of diabetes. It remains a major cause of new-onset visual loss in the United States and other industrialized nations. In Diabetic Retinopathy, Elia Duh and a panel of internationally recognized experts comprehensively assess the current state of knowledge regarding the clinical management of DR as well as its underlying mechanisms. The authors outline the current understanding of diabetic retinopathy from the perspective of clinical practice, while reviewing the multi-factorial pathogenesis and pathophysiology of... View Details
Diabetic Retinopathy: From Diagnosis to Treatment
by David S. Boyer MD (Author), Homayoun Tabandeh MD (Author)
The most common eye disease among those with type 1 or type 2 diabetes is diabetic retinopathy and this book explains the disease, how it develops, and options for treatment. Affecting more than five million Americans, the disease is caused by damage to the tiny blood vessels of the retina as the result of uncontrolled blood sugars, and is a leading cause of blindness. Diabetic retinopathy cannot be cured, however the onset can be delayed and the risk of progression can be reduced by keeping tight controls on glucose levels and making the right... View Details
Diabetic Retinopathy: The Essentials
by Gloria Wu MD (Author)
Diabetic Retinopathy: The Essentials is written for general ophthalmologists and optometrists as well as family practitioners, diabetologists, and internists who encounter diabetic patients on a daily basis. It focuses on the diagnosis and management of diabetic retinopathy from the point of view of the retinal specialist.
The book begins with the epidemiology, anatomy, and pathophysiology of diabetic retinopathy, and then covers important topics such as classification issues, diagnostic testing, examination techniques, new treatment modalities, patient management,... View Details
A Practical Manual of Diabetic Retinopathy Management (Practical Manual of Series)
by Peter H. Scanlon (Editor), Ahmed Sallam (Editor), Peter van Wijngaarden (Editor)
The incidence of diabetes is increasing worldwide at an alarming rate, and diabetic retinopathy is one of the most significant complications of diabetes. Packed with outstanding retinal photos, the second edition of this one-stop clinical manual offers a comprehensive overview of the diagnosis, treatment and long–term management of patients with diabetic eye disease.
Edited and authored by world-renowned experts from leading centres of excellence, A Practical Manual of Diabetic Retinopathy Management presents evidence-based guidance relevant for a... View Details
by Bruno, M.D. Lumbroso (Author), Marco, M.D. Rispoli (Author), Maria Cristina, M.D., Ph.D. Savastano (Author)
Diabetic Retinopathy is a concise guide to the condition caused by complications of diabetes, which can eventually lead to blindness. The book is edited by a team of experts from the Italian Macular Centre in Rome, led by Prof Bruno Lumbroso. Divided into ten chapters, the book begins with basic information on diabetes and diabetic retinopathy. Further chapters demonstrate how to read and interpret different forms of imaging, including fluorescein angiography, cross-section OCT, and 'en-face' OCT. Information on the use of the most recent OCT angiography imaging techniques brings this book... View Details
Diabetic Retinopathy: Evidence-Based Management
by David J. Browning (Editor)
Contains information from the Diabetic Retinopathy Clinical Research network not to be found in other published works
Evidence-based approach includes material labeled with level of supporting evidence and many clinical examples
Includes discussions of area of controversyView Details
Clinical Strategies in the Management of Diabetic Retinopathy: A step-by-step Guide for Ophthalmologists
by Francesco Bandello (Editor), Marco Attilio Zarbin (Editor), Rosangela Lattanzio (Editor), Ilaria Zucchiatti (Editor)
With the advent of effective treatments for diabetic retinopathy (DR), a new era in the management of DR has been opened up. Amid the deluge of approved treatments and promising new strategies, however, clinicians may find it difficult to choose the appropriate practice in each individual case. The purpose of this easy-to-use and richly illustrated manual is to assist ophthalmologists in making decisions in the entire management of DR based on the best available evidence. Practical and complete recommendations are provided to guide clinicians in diagnosis, decision-making, and treatment. The... View Details
Diabetic Eye Disease - Don't Go Blind From Diabetes: An easy to understand guide to keeping your vision for people with diabetes
by David Khorram MD (Author)
Millions of people go blind from diabetes. Make sure you're not one of them. In this book, award-winning ophthalmologist, writer and educator, Dr. David Khorram serves as your guide, giving you the knowledge you need to keep your vision for a lifetime.
This easy to understand guide begins with a description of how the eye works and how diabetes causes damage, not just in the eye but throughout the body. Dr. Khorram then discusses the stages of diabetic retinopathy, as well as other diabetic related problems such as cataracts, fluctuating vision and double vision. Dr. Khorram goes on to... View Details
Current Management of Diabetic Retinopathy, 1e
by Caroline R. Baumal MD (Author), Jay S. Duker MD (Author)
Stay current with recent progress in the field of diabetic retinopathy management with this practical resource by Drs. Caroline R. Baumal and Jay S. Duker. Concise, highly illustrated coverage includes summaries of the latest evidence and expert guidance on the rationale for each therapeutic option.Features a wealth of information for ophthalmologists, retinal specialists, and trainees on current management of this increasingly common condition.
Covers how to select the best course of action between drug, laser, or surgical treatment and how to achieve... View Details
Diabetic Retinopathy: A Practical Guide
by Jr. Flynn,William E. Smiddy,Ingrid Scott Harry W. (Author)