BMT 8DM00 and ASCI course a8, Fall 2017:
NEW: Exam tasks now available
Prof. Romeny retires
Symposium + Valedictory lecture 1 Dec 2017
Auditorium TU/e, 10:0015:00, 16:0017:00
Info and
registration
here (register today!)
A PhD / MSc course given at the Department of Biomedical Engineering of Eindhoven University of Technology.
Deep learning, selforganization and plasticity,
convolutional neural networks, geometry engine, contextual Gestalt
processing: the field known as 'braininspired computing' is one of the
most promising avenues in medical image computing today.

Tutors: 
Prof. Bart M. ter Haar
Romeny, Eindhoven University of Technology / Northeastern University
(Shenyang, China) Prof. Nicolai Petkov, University of Groningen 
Dates:  2 fulltime weeks: Lectures (each half day) from Monday 20 November 2017 till Friday 24 November 2017, and from Tuesday 5 December 2017 till Friday 8 December 2017. Computer laboratories (other each half day) using Mathematica 11. 
Registration: 
Register through the ASCI website:
here.
TU/e BME students: Register through the regular TU/e OASIS page for 8DM00. Registration for course and exam is free for TU/e, ASCI, NFBIA, ImagO students, and employees of industries officially collaborating with TU/e BME. For registration as 'contractant' with TU/e to do an official exam for 3 ECTS as nonTU/e student: see STU registration form. Costs: € 500 per course. Costs for industrial participants: € 1200 (invoice will be sent by ASCI after registration). 
Venue  Campus of Eindhoven University of Technology, Eindhoven, the Netherlands. Google Maps, TU/e campus map. 
Hotel  Suggestions (Booking.com) 
Total duration: 27 oral lectures of 45
minutes each, and 27 hours handson training.
Code ASCI: a8 (4 ECTS study points).
Code TUEBME:
8DM00
(2.5 ECTS study points).
Code MANET: Training (4 ECTS study
points).
Description:
We give insight in modern approaches to deep convolutional neural nets, and we will teach a number of wellestablished mathematical modeling techniques in detail, in particular multiscale and multiorientation differential geometry, models for selforganization and plasticity, and geometric neural feedback, leading to effective adaptive operations. We present the theory in an axiomatic, intuitive and fundamentally understood way.
This interactive course is interspersed with
working and powerful applications in medical image analysis, such as
computeraided detection of breast tumors, invariant feature detection,
development of retinal vessel biomarkers, and contextual Gestaltbased operators
to deal with missing data.
You will learn how geometric reasoning
works and can be applied. We design image analysis algorithms by
carefully studying the requirements, physical analogies, and in particular by
looking how our visual system does it. After all, this is still the best
performing recognition computer we know, even for noisy, partly missing
(occluded) data, low contrast etc. Modern (often optical) brain imaging methods
will be discussed (voltage sensitive dyes, optogenetics, fMRI, DTI/HARDI) and recent discoveries of functional brain mechanisms in
visual perception.
The majority of the examples discussed are from 2D, 3D and 4D (3Dtime) medical imaging. We devote some time to the efficient numerical implementation of the different techniques. Handson experience is acquired in a computer lab. We use Mathematica 11 as this software suite is eminently suited for this design process. We experiment handson with virtually all aspects discussed in the course.
Some applications discussed:
Detailed program and content:
Times: 
 
*Lecture rooms at TU/e campus, see map: FLX: Flux building (Applied Physics & Electrical Engineering) GEMZ: Gemini South building (Biomedical Engineering & Mechanical Engineering) GEMN: Gemini North building (same entrance as GeminiSouth). HEO: HelixEast bulding (ST  Chemical Technology) HEW: HelixWest building (ST  Chemical Technology) Matrix: Matrix building. 
The reader consists of the chapters of a the book: "FrontEnd Vision and Multiscale
Image Analysis", by Bart M. ter Haar Romeny. This book is written as a series of
Mathematica notebooks. It contains a CRROM
with all notebooks, which can be installed in the Mathematica Helpbrowser. The
Mathematica code is the
topic for the computer laboratories during the course. ISBN: 1402015070 (paperback), 1402015038 (hardcover). Springer,
Berlin. 
Recommended reading (books):
Deep
Learning in Medical Image Analysis
Special issue of IEEE Transactions on Medical Imaging, volume 35, issue 5,
May 2016.
Of special interest is the introductory text:
Hayit Greenspan, Bram van Ginneken, Ronald M. Summers, ‘Guest Editorial
Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting
New Technique’, IEEETMI, 355, 11531159.
Recommended reading (websites):
Software:
Other:
Computer laboratories will be organized to acquire handson experience with all discussed topics on a variety of 2D and 3D images. We use the program Mathematica 11 (www.wolfram.com).
For TUE members: Mathematica 11 for
Windows can be
downloaded from the
TU/e campus software website.
Mac OS x version: \\physstor\appl\macsoftware.
Linux version:
\\wtbfiler\Software\UnixSoftware.
Recommended introductory tutorial books on Mathematica:
Some useful notebooks:
The famous mathematics teaching files and resource online: MathWorld.
Select any three (3) questions from this set of questions: ExamTasks_FEV2017.nb.
Write a Mathematica 11 notebook per question and send them,
preferably within two weeks
after the end of the course, to B.M.terHaarRomeny@tue.nl.
Please explain the steps of your reasoning in detail, use Manipulate functions
if appropriate.
Make sure the notebook can run, so include your own images (store them in the
same directory as the notebook, and use SetDirectory[NotebookDirectory[ ]]), or
Import them from a web URL.
The detection of ridges (midlines) for an Xray image of hands. "Ridgeness" is a second order property. 
Low dose fluoroscopy image of an electrophysiology catheter in ther heart. The extra low dose is beneficial for the radiation dose, but leads to a deteriorated image quality.  Robust catheter detection with oriented filters and tensor voting. 
Left: histological image of a fungus cell, paramecium caudatum.

Noisy 2photon microscopy image of bone tissue. 
The same image enhanced with an orientationscore denoising filter. 
prof. Bart M. ter Haar Romeny,
PhD Department of Biomedical Engineering (BMEBMT) Group Biomedical Imaging Analysis BMIA Eindhoven University of Technology De Rondom 70  GEMZ 2.106 NL5612 AP Eindhoven, Netherlands Tel. +31402475537 (secr. Rina van Dijck) email: B.M.terHaarRomeny@tue.nl

prof. Nicolai Petkov, PhD Department of Computing Science Rijks Universiteit Groningen Blauwborgje 3 NL9700 AV Groningen, Netherlands Tel: 0031503633939 (secr.) / 3637129 / 3633931 Fax: +31503633800 Email: N.Petkov@rug.nl 
Click for larger version.
Class of Fall 2017.