4 Different tutorials (half day each) are planned for ISBI 2017 during the first day of the conference (Tuesday April 18th 2017). Medium and large rooms are available to hold 120/240 or 63/96 attendees in either theatre or class room configurations respectively. Please have a look at previous ISBI tutorials.


  • Andrew Zalesky, The University of Melbourne, Australia
  • Oscar Acosta, University Rennes 1, France

Tutorial 1: Neuroimaging analysis within R

Dr John Muschelli, Johns Hopkins Bloomberg School of Public Health
Dr Jean-Phillipe Fortin, University of Pennsylvania

We will provide tutorials on how to use R for structural magnetic resonance imaging (MRI) analysis. We will show how to perform entire image analysis in R, from the scans in raw image format to the statistical analysis after image preprocessing, with an emphasis on reproducibility by using a single programming language. This course will use a real multiple sclerosis dataset and will show the steps of going from the raw image files to performing multiple sclerosis lesion classification with a number of classifiers entirely in R. In this hands-on tutorial, attendees will be given instructions for setup and data before the course, so that they are able to follow along and perform the analysis during the tutorial.


  • Introduction to the Statistical Software R
  • Read and Write Images
  • Visualization
  • Inhomogeneity Correction
  • Brain Extraction
  • Image Segmentation
  • Coregistration Within and Between MRI Studies
  • Intensity Normalization
  • Harmonization of mutli-site datasets

Tutorial 2: Biomedical texture analysis

Professor Adrien Depeursinge, EPFL
Associate Professor Weidong Cai, University of Sydney

Texture based imaging biomarkers complement focal, invasive biopsy based biomarkers by providing information on tissue structure over broad regions, non-invasively. Texture has been used to predict patient survival, tissue function, disease subtypes and genomics. Nevertheless, several challenges remain, such as: the lack of an appropriate framework for multi-scale, multi-spectral analysis in 2D and 3D; and, localization of uncertainty of texture operators. This tutorial will establish the mathematical foundations of multidimensional texture operators and will review the limitations of popular methods, including deep convolutional neural networks. We will describe recent research to address the major challenges outlined above. We will also demonstrate a number of medical applications and existing publicly available code and databases. Several clinicians will present potential high-impact clinical applications.

Part A – Image processing

  • Fundamentals and review of digital texture processing methods for biomedical image analysis
  • Texture analysis methods for different biomedical imaging modalities

Part B – Applications

  • Diagnosis of brain disorders based on textures in neuroimaging
  • Classification of histopathology images with texture encoding
  • Image texture information in pharmaceutical research

Tutorial 3: Continuous domain sparse recovery of biomedical image data using structured low-rank approaches

Professor Jong Chul Ye, KAIST, Korea
Associate Professor Mathews Jacob, University of Iowa

In this tutorial, we will provide a detailed review of recent advances in structured low-rank matrix completion formulation, where the recovery of a continuous domain image from its sparse measurements is considered. This framework is centered on the fundamental duality between the sparsity of the continuous signal in the spatial domain and the low-rankness of a structured matrix in the spectral (Fourier) domain. This property enables us to reformulate sparse recovery of continuous domain signal as a low-rank matrix completion problem in the spectral domain, thus providing the benefit of sparse recovery with performance guarantees. The proposed low-rank completion approach can be regarded as a generalization of recent spectral compressed sensing to recover large classes of finite rate of innovations (FRI) signals at near optimal sampling rates. We will also review fast algorithms that are comparable in complexity to current compressed sensing methods, which enable the application of the framework to large scale biomedical image recovery problems. We will demonstrate the utility of the framework in a wide range of biomedical imaging applications such as compressed sensing MRI, super-resolution microscopy, ultrasound, as well as other image processing applications.


  1. Introduction
  2. Review of compressed sensing
  3. FRI signal recovery from uniform samples
    3.1.  Review of 1-D FRI sampling theory
    3.2.  2-D FRI: Generalization to multidimensional signals
  4. Structured low-rank interpolation for non-uniform samples
    4.1.  1-D Theory
    4.2.  2-D Theory
  5. Fast Implementations
  6. Biomedical imaging applications
    6.1.  MRI – Part 1
    6.1.1.  Super-resolution MRI
    6.1.2.  CS-MRI using structured low-rank penalty
    6.2.  MRI – Part 2
    6.2.1.  CS MRI as a low-rank interpolation
    6.2.2.  MR artifact removal
    6.3.  Other biomedical imaging applications: super-resolution microscopy, scanning microscopy, X-ray CT, ultrasound imaging