From Swiss Federal Institute of Technology in Lausanne [EPFL-École Polytechnique Fédérale de Lausanne] (CH): “Deep-learning-based image analysis is now just a click away”

From Swiss Federal Institute of Technology in Lausanne [EPFL-École Polytechnique Fédérale de Lausanne] (CH)

Cécilia Carron

Under an initiative by EPFL’s Center for Imaging, a team of engineers from EPFL and Universidad Carlos III de Madrid have developed a plugin that makes it easier to incorporate artificial intelligence into image analysis for life-science research. The plugin, called deepImageJ, is described in a paper appearing today in Nature Methods.

Over the past five years, image analysis has been shifting away from traditional mathematical- and observational-based methods towards data-driven processing and artificial intelligence. This major development is making the detection and identification of valuable information in images easier, faster, and increasingly automated – in just about every research field. When it comes to life science, deep-learning-, a sub-field of artificial intelligence, is showing an increasing potential for bioimage analysis. Unfortunately, using the deep-learning models often requires coding skills that few life scientists possess. To make the process easier, image analysis experts from EPFL and University Carlos III of Madrid [Universidad Carlos III de Madrid](ES), working in association with EPFL’s Center for Imaging, have developed deepImageJ – an open-source plugin that’s described in a paper published today in Nature Methods.

Using neural networks in biomedical research

Deep-learning models are a significant breakthrough for the many fields that rely on imaging, such as diagnostics and drug development. In bio-imaging, for example, deep learning can be used to process vast collections of images and detect lesions in organic tissue, identify synapses between nerve cells, and determine the structure of cell membranes and nuclei. It’s ideal for recognizing and classifying images, identifying specific elements, and predicting experimental results.

This type of artificial intelligence involves training a computer to perform a task by drawing on large amounts of previously annotated data. It’s similar to CCTV systems that perform facial recognition, or to mobile-camera apps that enhance photos. Deep-learning models are based on sophisticated computational architectures called artificial neural networks that can be trained for specific research purposes, such as to recognize certain types of cells or tissue lesions or to improve image quality. The trained neural network is then saved as a computer model.

Artificial intelligence, but without the code

For biomedical imaging, a consortium of European researchers is developing a repository of these pre-trained deep-learning models, called the BioImage Model Zoo. “To train these models, researchers need specific resources and technical knowledge – especially in Python coding – that many life scientists do not have,” says Daniel Sage, the engineer at EPFL’s Center for Imaging who is overseeing the deepImageJ development. “But ideally, these models should be available to everyone.”

The deepImageJ plugin bridges the gap between artificial neural networks and the researchers who use them. Now, a life scientist can ask a computer engineer to design and train a machine-learning algorithm to perform a specific task, which the scientist can then easily run via a user interface – without ever seeing a single line of code. The plugin is open-source and free-of-charge, and will speed the dissemination of new developments in computer science and the publication of biomedical research. It is designed to be a collaborative resource that enables engineers, computer scientists, mathematicians and biologists to work together more efficiently. For example, a model developed recently by an EPFL Master’s student, working as part of a cross-disciplinary team, enables scientists to distinguish human cells from mouse cells in tissue sections.

Researchers can train users, too

Life scientists around the world have been hoping for such a system for several years, but – until EPFL’s Center for Imaging stepped in – no one had taken up the challenge of building one. The research group is headed by Daniel Sage and Michael Unser, the Center’s academic director, together with Arrate Muñoz Barrutia, associate professor at UC3M. Professor Muñoz-Barrutia led the operational development work along with one of her PhD students, Estibaliz Gómez-de-Mariscal, and Carlos García López de Haro, a bioengineering research assistant .

So that as many researchers can use the plugin as possible, the group is also developing virtual seminars, training materials and online resources, with a view to better exploiting the full potential of artificial intelligence. These materials are being designed with both programmers and life scientists in mind, so that users can quickly come to grips with the new method. DeepImageJ will also be presented at ZIDAS – a week-long class on image and data analysis for life scientists in Switzerland.

See the full article here .


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The Swiss Federal Institute of Technology in Lausanne [EPFL-École polytechnique fédérale de Lausanne] (CH) is a research institute and university in Lausanne, Switzerland, that specializes in natural sciences and engineering. It is one of the two Swiss Federal Institutes of Technology, and it has three main missions: education, research and technology transfer.

The QS World University Rankings ranks EPFL(CH) 14th in the world across all fields in their 2020/2021 ranking, whereas Times Higher Education World University Rankings ranks EPFL(CH) as the world’s 19th best school for Engineering and Technology in 2020.

EPFL(CH) is located in the French-speaking part of Switzerland; the sister institution in the German-speaking part of Switzerland is the Swiss Federal Institute of Technology ETH Zürich [Eidgenössische Technische Hochschule Zürich)](CH) . Associated with several specialized research institutes, the two universities form the Domain of the Swiss Federal Institutes of Technology (ETH Domain) [ETH-Bereich; Domaine des Écoles polytechniques fédérales] (CH) which is directly dependent on the Federal Department of Economic Affairs, Education and Research. In connection with research and teaching activities, EPFL(CH) operates a nuclear reactor CROCUS; a Tokamak Fusion reactor; a Blue Gene/Q Supercomputer; and P3 bio-hazard facilities.

ETH Zürich, EPFL (Swiss Federal Institute of Technology in Lausanne) [École polytechnique fédérale de Lausanne](CH), and four associated research institutes form the Domain of the Swiss Federal Institutes of Technology (ETH Domain) [ETH-Bereich; Domaine des Écoles polytechniques fédérales] (CH) with the aim of collaborating on scientific projects.

The roots of modern-day EPFL(CH) can be traced back to the foundation of a private school under the name École spéciale de Lausanne in 1853 at the initiative of Lois Rivier, a graduate of the École Centrale Paris (FR) and John Gay the then professor and rector of the Académie de Lausanne. At its inception it had only 11 students and the offices was located at Rue du Valentin in Lausanne. In 1869, it became the technical department of the public Académie de Lausanne. When the Académie was reorganised and acquired the status of a university in 1890, the technical faculty changed its name to École d’ingénieurs de l’Université de Lausanne. In 1946, it was renamed the École polytechnique de l’Université de Lausanne (EPUL). In 1969, the EPUL was separated from the rest of the University of Lausanne and became a federal institute under its current name. EPFL(CH), like ETH Zürich(CH), is thus directly controlled by the Swiss federal government. In contrast, all other universities in Switzerland are controlled by their respective cantonal governments. Following the nomination of Patrick Aebischer as president in 2000, EPFL(CH) has started to develop into the field of life sciences. It absorbed the Swiss Institute for Experimental Cancer Research (ISREC) in 2008.

In 1946, there were 360 students. In 1969, EPFL(CH) had 1,400 students and 55 professors. In the past two decades the university has grown rapidly and as of 2012 roughly 14,000 people study or work on campus, about 9,300 of these being Bachelor, Master or PhD students. The environment at modern day EPFL(CH) is highly international with the school attracting students and researchers from all over the world. More than 125 countries are represented on the campus and the university has two official languages, French and English.


EPFL is organised into eight schools, themselves formed of institutes that group research units (laboratories or chairs) around common themes:

School of Basic Sciences (SB, Jan S. Hesthaven)

Institute of Mathematics (MATH, Victor Panaretos)
Institute of Chemical Sciences and Engineering (ISIC, Emsley Lyndon)
Institute of Physics (IPHYS, Harald Brune)
European Centre of Atomic and Molecular Computations (CECAM, Ignacio Pagonabarraga Mora)
Bernoulli Center (CIB, Nicolas Monod)
Biomedical Imaging Research Center (CIBM, Rolf Gruetter)
Interdisciplinary Center for Electron Microscopy (CIME, Cécile Hébert)
Max Planck-EPFL Centre for Molecular Nanosciences and Technology (CMNT, Thomas Rizzo)
Swiss Plasma Center (SPC, Ambrogio Fasoli)
Laboratory of Astrophysics (LASTRO, Jean-Paul Kneib)

School of Engineering (STI, Ali Sayed)

Institute of Electrical Engineering (IEL, Giovanni De Micheli)
Institute of Mechanical Engineering (IGM, Thomas Gmür)
Institute of Materials (IMX, Michaud Véronique)
Institute of Microengineering (IMT, Olivier Martin)
Institute of Bioengineering (IBI, Matthias Lütolf)

School of Architecture, Civil and Environmental Engineering (ENAC, Claudia R. Binder)

Institute of Architecture (IA, Luca Ortelli)
Civil Engineering Institute (IIC, Eugen Brühwiler)
Institute of Urban and Regional Sciences (INTER, Philippe Thalmann)
Environmental Engineering Institute (IIE, David Andrew Barry)

School of Computer and Communication Sciences (IC, James Larus)

Algorithms & Theoretical Computer Science
Artificial Intelligence & Machine Learning
Computational Biology
Computer Architecture & Integrated Systems
Data Management & Information Retrieval
Graphics & Vision
Human-Computer Interaction
Information & Communication Theory
Programming Languages & Formal Methods
Security & Cryptography
Signal & Image Processing

School of Life Sciences (SV, Gisou van der Goot)

Bachelor-Master Teaching Section in Life Sciences and Technologies (SSV)
Brain Mind Institute (BMI, Carmen Sandi)
Institute of Bioengineering (IBI, Melody Swartz)
Swiss Institute for Experimental Cancer Research (ISREC, Douglas Hanahan)
Global Health Institute (GHI, Bruno Lemaitre)
Ten Technology Platforms & Core Facilities (PTECH)
Center for Phenogenomics (CPG)
NCCR Synaptic Bases of Mental Diseases (NCCR-SYNAPSY)

College of Management of Technology (CDM)

Swiss Finance Institute at EPFL (CDM-SFI, Damir Filipovic)
Section of Management of Technology and Entrepreneurship (CDM-PMTE, Daniel Kuhn)
Institute of Technology and Public Policy (CDM-ITPP, Matthias Finger)
Institute of Management of Technology and Entrepreneurship (CDM-MTEI, Ralf Seifert)
Section of Financial Engineering (CDM-IF, Julien Hugonnier)

College of Humanities (CDH, Thomas David)

Human and social sciences teaching program (CDH-SHS, Thomas David)

EPFL Middle East (EME, Dr. Franco Vigliotti)[62]

Section of Energy Management and Sustainability (MES, Prof. Maher Kayal)

In addition to the eight schools there are seven closely related institutions

Swiss Cancer Centre
Center for Biomedical Imaging (CIBM)
Centre for Advanced Modelling Science (CADMOS)
École cantonale d’art de Lausanne (ECAL)
Campus Biotech
Wyss Center for Bio- and Neuro-engineering
Swiss National Supercomputing Centre