Page tree
Skip to end of metadata
Go to start of metadata

About the course

Team: Beatrice Demiray, Javier Esteban, Hendrik Burwinkel (Contact the course tutors)

6 SWS (10 ECTS)

TUMOnline

Announcements

01.08.19: The Matching has ended and registration is now closed.

Schedule

For workshops and presentation schedule, please refer to TUMOnline.


24.10.19: Project application deadline, students are required to indicate 3 project preferences. Use the Student Application Template provided in the Download section. More information will be given in the introductory session.

28.10.19: Project asssignments published on this website and via e-mail.

Intermediate Presentation Schedule

7 minutes slot for each presentation (plus 4 minutes for questions). We encourage students to practice their presentation w.r.t. pace and time. In order to train compliance with speaking times, your presentation will be interrupted after 7 minutes 30 seconds, no exceptions made. Make sure to comply with the TUM Code of Conduct (see Downloads) and the rules for this course (slides from the first lecture).

Vol. 1: Monday, 02.12.2019

IDStudentTitleSupervisor
1OliverDeep learning-based detection and classification of metastases in 3D scans of tumor-bearing miceOliver Schoppe
2DmitryDataset Generation for Deep Learning Instance Segmentation ModelMartin Sundermeyer
3DhavalDeep neural network-based detection of cerebral aneurysms in MR AngiographySuprosanna Shit, Augusto Fava Sanches
4MariaTranscutaneous Electrical StimulationBàlint Varkuti
5YupengQuantification of water MR relaxation parameters in fat-containing tissuesDimitrios Karampinos
6LeonImplementation of a Haptic Feedback deviceJavier Esteban
7Fernando Invertible DLWalter Simson
8

Mirac

Brain lesion segmentation tool with deep-learning kernel

Hongwei Li

9Michael AR-based gesture controlAlejandro Martin Gomez


Vol. 2: Monday, 09.12.2019

IDStudentTitleSupervisor
1Kristina

Landmark detection on X-ray images

Javier Esteban, Matthias Grimm
2TheofilosVessel Quantification MRIBeatrice Demiray
3AlessaSpine Reconstruction using Deep LearningThomas Wendler
4

Federico

Detection of diabetic retinopathy on fundus images obtained with smartphone camera

Gerome Vivar
5SubhadarshiniAutomatic 3D Segmentation of Spinal CordThomas Wendler
6FelixVisualizing blood vessels for augmented reality laparoscopyMatthias Grimm
7LemoniaAtlas-based Spine ReconstructionThomas Wendler
8UmutDeep Generative Model for Longitudinal AnalysisSeong Tae Kim
9BorjaSpatio-temporal deep network for early disease detectionSeong Tae Kim


Final Presentation Schedule


Vol. 1: Monday, 27.01.2020

IDStudentTitleSupervisor
1

Mirac

Brain lesion segmentation tool with deep-learning kernel

Hongwei Li

2Michael AR-based gesture controlAlejandro Martin Gomez
3Fernando Invertible DLWalter Simson
4UmutDeep Generative Model for Longitudinal AnalysisSeong Tae Kim
5TheofilosVessel Quantification MRIBeatrice Demiray
6MariaTranscutaneous Electrical StimulationBàlint Varkuti
7FelixVisualizing blood vessels for augmented reality laparoscopyMatthias Grimm
8OliverDeep learning-based detection and classification of metastases in 3D scans of tumor-bearing miceOliver Schoppe
9DhavalDeep neural network-based detection of cerebral aneurysms in MR AngiographySuprosanna Shit, Augusto Fava Sanches


Vol. 2: Monday, 03.02.2020

IDStudentTitleSupervisor
1LemoniaAtlas-based Spine ReconstructionThomas Wendler
2BorjaSpatio-temporal deep network for early disease detectionSeong Tae Kim
3YupengQuantification of water MR relaxation parameters in fat-containing tissuesDimitrios Karampinos
4DmitryDataset Generation for Deep Learning Instance Segmentation ModelMartin Sundermeyer
5LeonImplementation of a Haptic Feedback deviceJavier Esteban
6AlessaSpine Reconstruction using Deep LearningThomas Wendler
7Kristina

Landmark detection on X-ray images

Javier Esteban, Matthias Grimm
8

Federico

Detection of diabetic retinopathy on fundus images obtained with smartphone camera

Gerome Vivar
9SubhadarshiniAutomatic 3D Segmentation of Spinal CordThomas Wendler


Materials

Introduction

Lecture 1 - Project Management

Lecture 2 - UML

Available Projects

Projects will be announced in due time and presented during the introductory meeting. Please communicate a selection of 3 choices, each with an explanation (ca. 5 sentences) for why you want to work on this project and how you meet the requirements specified in the project proposal. Use the Student Application Template provided in the Download section.


IDTitleSupervisorStudentPDFComments
1Deep Generative Model for Longitudinal AnalysisSeong Tae KimUmutSeong Tae - Longitudinal Analisis
2Spatio-temporal deep network for early disease detectionSeong Tae KimBorjaSeong Tae - Disease Detection
3Real or Fake? A Webplatform for Visual Turing TestsChristoph Baur
Baur - Real or Fake?
4Brain lesion segmentation tool with deep-learning kernelHongwei LiMiracHongwei Li - Brain Segmentation
5Visualizing blood vessels for augmented reality laparoscopyMatthias GrimmFelixGrimm - Vessel Visualization
6Collimator Design PlatformThomas Wendler
Wendler - Collimators
7Monte Carlo simulations for BrachytherapyThomas Wendler
Wendler - MonteCarlo
8Automatic 3D Segmentation of Spinal CordThomas WendlerSubhadarshiniWendler - Spinal Cord 3D Seg.
9Atlas-based Spine ReconstructionThomas WendlerLemoniaWendler - Spine Reconstruction Atlas
10Spine Reconstruction using Deep LearningThomas WendlerAlessaWendler - Spine Reconstruction DL
11Vessel Quantification MRIBeatrice DemirayTheofilosDemiray - Vessel Quantification MRI
12Deployment of deep learning CNN based algorithms to a mobile appTobias Czempiel, Matthias Keicher
Czempiel, Keicher - DL to Mobile appUp to 2 students
13

Landmark detection on X-ray images

Javier Esteban, Matthias GrimmKristinaEsteban - X-ray Landmark detection
14Towards fully automatic Robotic-US scan for ThyroidJavier Esteban
Esteban - Thyroid Robotic US
15Semantic Inside-Out TrackingBenjamin Busam
Busam - Semantic IO Tracker
16AR-based gesture controlAlejandro Martin GomezMichael 

17Quantification of water MR relaxation parameters in fat-containing tissuesDimitrios KarampinosYupeng

18Dataset Generation for Deep Learning Instance Segmentation ModelMartin SundermeyerDmitry

19Transcutaneous Electrical StimulationBàlint VarkutiMaria

20Deep neural network-based detection of cerebral aneurysms in MR AngiographySuprosanna Shit, Augusto Fava SanchesDhaval

21Detection of diabetic retinopathy on fundus images obtained with smartphone cameraGerome VivarFederico

22Invertible DLWalter SimsonFernando 

23Implementation of a Haptic Feedback deviceJavier EstebanLeon



24Deep learning-based detection and classification of metastases in 3D scans of tumor-bearing miceOliver SchoppeOliver

Preliminary Meeting

When: Tuesday, 09.07.2019 at 11:00 - 12:00 CEST

Where: CAMP Seminar Room  03.13.010

Download slides

Prerequisites and Registration

This course requires basic knowledge of C++ or similar OO programming language. The concepts of OO programming and other concepts as conducted in the Introduction to Computer Science lecture are assumed.

Registration through the TUM Matching System is mandatory. Your chances to be assigned to the course increase if you give the course a higher rank in your choices. If you already have a potential project, notify the course tutors via e-mail as soon as possible. This increases your chances to be assigned to the course, but you have to register through the matching system in any case. For further details about how the matching system works and its schedule please check the documentation.

Project Proposals

In this lab course, students work on clinical software projects and find solutions for problems in the field of medical applications. The student's workload should be around 10-14 hours per week for a period of 3 - 4 months on the assigned project (10 ECTS course).

Each project is expected to have one contact person (supervisor) who should be available for regular updates or if the student needs any help. In addition, the supervisors are kindly invited to participate in the student presentations (Requirements, Intermediate, Final) taking place at the CAMP chair. Each project supervisor will decide on 50% of the grade based on the student’s performance in the project. The other 50% will be decided upon by lecturers, based on the quality of project management and presentation skills of the student.

The project proposal should give a short introduction into the context and describe the most important aspects and expected outcome of the project. Furthermore it should clearly state the required (and optional) skills (programming language, experience with certain libraries etc.).

If you have a suitable project, preferably with a clinical or industrial partner, you are kindly invited to fill out the project proposal template and send it to the course tutors. The deadline for proposal submission is Sunday, September 8th, 2019.

Downloads

* TUM Informatics Student Code of Conduct
* TUM Citation Guide
* CAMP presentation slides for PowerPoint
* CAMP presentation slides for Latex
* Student project application template
* Supervisor project proposal template

Literature and Resources

C++

Online C++ API reference

C++ Tutorials

CMake

Python

Getting started with Python

The Python Tutorial

Learn Python

Codecademy

Anaconda

Documentation and Coding Guidelines

Doxygen

Google C++ Style Guide

IDE

MS Visual Studio can be downloaded from TUM StudiSoft.

PyCharm

Codeblocks

Eclipse

Github

Github

With your TUM account you can use the LRZ Gitlab

Qt

Qt Reference Documentation

Other useful tools and libraries

OpenCV Documentation

Insight Segmentation and Registration Toolkit (ITK)

Visualization Toolkit (VTK)

Docker software containers

  • No labels