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, 10:00-15:00, 16:00-17:00
here (register today!)
A PhD / MSc course given at the Department of Biomedical Engineering of Eindhoven University of Technology.
Deep learning, self-organization and plasticity,
convolutional neural networks, geometry engine, contextual Gestalt
processing: the field known as 'brain-inspired computing' is one of the
most promising avenues in medical image computing today.
Prof. Bart M. ter Haar
Romeny, Eindhoven University of Technology / Northeastern University
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.|
|Register through the ASCI website:
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 non-TU/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.|
Total duration: 27 oral lectures of 45
minutes each, and 27 hours hands-on training.
Code ASCI: a8 (4 ECTS study points).
Code TUE-BME: 8DM00 (2.5 ECTS study points).
Code MANET: Training (4 ECTS study points).
We give insight in modern approaches to deep convolutional neural nets, and we will teach a number of well-established mathematical modeling techniques in detail, in particular multi-scale and multi-orientation differential geometry, models for self-organization 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
computer-aided detection of breast tumors, invariant feature detection,
development of retinal vessel biomarkers, and contextual Gestalt-based 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, opto-genetics, 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 (3D-time) medical imaging. We devote some time to the efficient numerical implementation of the different techniques. Hands-on experience is acquired in a computer lab. We use Mathematica 11 as this software suite is eminently suited for this design process. We experiment hands-on with virtually all aspects discussed in the course.
Some applications discussed:
Detailed program and content:
*Lecture rooms at TU/e campus, see map:
FLX: Flux building (Applied Physics & Electrical Engineering)
GEM-Z: Gemini South building (Biomedical Engineering & Mechanical Engineering)
GEM-N: Gemini North building (same entrance as Gemini-South).
HEO: Helix-East bulding (ST - Chemical Technology)
HEW: Helix-West building (ST - Chemical Technology)
Matrix: Matrix building.
|The reader consists of the chapters of a the book: "Front-End Vision and Multi-scale Image Analysis", by Bart M. ter Haar Romeny. This book is written as a series of Mathematica notebooks. It contains a CR-ROM with all notebooks, which can be installed in the Mathematica Help-browser. The Mathematica code is the topic for the computer laboratories during the course.|
Recommended reading (books):
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’, IEEE-TMI, 35-5, 1153-1159.
Recommended reading (websites):
Computer laboratories will be organized to acquire hands-on 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\mac-software.
Linux version: \\wtbfiler\Software\Unix-Software.
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 X-ray image of hands. "Ridgeness" is a second order property.
|Low dose fluoroscopy image of an electro-physiology 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 2-photon microscopy image of bone tissue.
The same image enhanced with an orientation-score denoising filter.
|prof. Bart M. ter Haar Romeny,
Department of Biomedical Engineering (BME-BMT)
Group Biomedical Imaging Analysis BMIA
Eindhoven University of Technology
De Rondom 70 - GEM-Z 2.106
NL-5612 AP Eindhoven, Netherlands
Tel. +31-40-2475537 (secr. Rina van Dijck)
|prof. Nicolai Petkov, PhD
Department of Computing Science
Rijks Universiteit Groningen
NL-9700 AV Groningen, Netherlands
Tel: 00-31-50-3633939 (secr.) / 3637129 / 3633931
Click for larger version.
Class of Fall 2017.