PMSD: Project Management and Software Development for Medical Applications (winter 2019/20)

About the course

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

6 SWS (10 ECTS)

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

ID	Student	Title	Supervisor
1	Oliver	Deep learning-based detection and classification of metastases in 3D scans of tumor-bearing mice	Oliver Schoppe
2	Dmitry	Dataset Generation for Deep Learning Instance Segmentation Model	Martin Sundermeyer
3	Dhaval	Deep neural network-based detection of cerebral aneurysms in MR Angiography	Suprosanna Shit, Augusto Fava Sanches
4	Maria	Transcutaneous Electrical Stimulation	Bàlint Varkuti
5	Yupeng	Quantification of water MR relaxation parameters in fat-containing tissues	Dimitrios Karampinos
6	Leon	Implementation of a Haptic Feedback device	Javier Esteban
7	Fernando	Invertible DL	Walter Simson
8	Mirac	Brain lesion segmentation tool with deep-learning kernel	Hongwei Li
9	Michael	AR-based gesture control	Alejandro Martin Gomez

Vol. 2: Monday, 09.12.2019

ID	Student	Title	Supervisor
1	Kristina	Landmark detection on X-ray images	Javier Esteban, Matthias Grimm
2	Theofilos	Vessel Quantification MRI	Beatrice Demiray
3	Alessa	Spine Reconstruction using Deep Learning	Thomas Wendler
4	Federico	Detection of diabetic retinopathy on fundus images obtained with smartphone camera	Gerome Vivar
5	Subhadarshini	Automatic 3D Segmentation of Spinal Cord	Thomas Wendler
6	Felix	Visualizing blood vessels for augmented reality laparoscopy	Matthias Grimm
7	Lemonia	Atlas-based Spine Reconstruction	Thomas Wendler
8	Umut	Deep Generative Model for Longitudinal Analysis	Seong Tae Kim
9	Borja	Spatio-temporal deep network for early disease detection	Seong Tae Kim

Final Presentation Schedule

Vol. 1: Monday, 27.01.2020

ID	Student	Title	Supervisor
1	Mirac	Brain lesion segmentation tool with deep-learning kernel	Hongwei Li
2	Michael	AR-based gesture control	Alejandro Martin Gomez
3	Fernando	Invertible DL	Walter Simson
4	Umut	Deep Generative Model for Longitudinal Analysis	Seong Tae Kim
5	Theofilos	Vessel Quantification MRI	Beatrice Demiray
6	Maria	Transcutaneous Electrical Stimulation	Bàlint Varkuti
7	Felix	Visualizing blood vessels for augmented reality laparoscopy	Matthias Grimm
8	Oliver	Deep learning-based detection and classification of metastases in 3D scans of tumor-bearing mice	Oliver Schoppe
9	Dhaval	Deep neural network-based detection of cerebral aneurysms in MR Angiography	Suprosanna Shit, Augusto Fava Sanches

Vol. 2: Monday, 03.02.2020

ID	Student	Title	Supervisor
1	Lemonia	Atlas-based Spine Reconstruction	Thomas Wendler
2	Borja	Spatio-temporal deep network for early disease detection	Seong Tae Kim
3	Yupeng	Quantification of water MR relaxation parameters in fat-containing tissues	Dimitrios Karampinos
4	Dmitry	Dataset Generation for Deep Learning Instance Segmentation Model	Martin Sundermeyer
5	Leon	Implementation of a Haptic Feedback device	Javier Esteban
6	Alessa	Spine Reconstruction using Deep Learning	Thomas Wendler
7	Kristina	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
9	Subhadarshini	Automatic 3D Segmentation of Spinal Cord	Thomas 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.

ID	Title	Supervisor	Student	PDF	Comments
1	Deep Generative Model for Longitudinal Analysis	Seong Tae Kim	Umut	Seong Tae - Longitudinal Analisis
2	Spatio-temporal deep network for early disease detection	Seong Tae Kim	Borja	Seong Tae - Disease Detection
3	Real or Fake? A Webplatform for Visual Turing Tests	Christoph Baur		Baur - Real or Fake?
4	Brain lesion segmentation tool with deep-learning kernel	Hongwei Li	Mirac	Hongwei Li - Brain Segmentation
5	Visualizing blood vessels for augmented reality laparoscopy	Matthias Grimm	Felix	Grimm - Vessel Visualization
6	Collimator Design Platform	Thomas Wendler		Wendler - Collimators
7	Monte Carlo simulations for Brachytherapy	Thomas Wendler		Wendler - MonteCarlo
8	Automatic 3D Segmentation of Spinal Cord	Thomas Wendler	Subhadarshini	Wendler - Spinal Cord 3D Seg.
9	Atlas-based Spine Reconstruction	Thomas Wendler	Lemonia	Wendler - Spine Reconstruction Atlas
10	Spine Reconstruction using Deep Learning	Thomas Wendler	Alessa	Wendler - Spine Reconstruction DL
11	Vessel Quantification MRI	Beatrice Demiray	Theofilos	Demiray - Vessel Quantification MRI
12	Deployment of deep learning CNN based algorithms to a mobile app	Tobias Czempiel, Matthias Keicher		Czempiel, Keicher - DL to Mobile app	Up to 2 students
13	Landmark detection on X-ray images	Javier Esteban, Matthias Grimm	Kristina	Esteban - X-ray Landmark detection
14	Towards fully automatic Robotic-US scan for Thyroid	Javier Esteban		Esteban - Thyroid Robotic US
15	Semantic Inside-Out Tracking	Benjamin Busam		Busam - Semantic IO Tracker
16	AR-based gesture control	Alejandro Martin Gomez	Michael
17	Quantification of water MR relaxation parameters in fat-containing tissues	Dimitrios Karampinos	Yupeng
18	Dataset Generation for Deep Learning Instance Segmentation Model	Martin Sundermeyer	Dmitry
19	Transcutaneous Electrical Stimulation	Bàlint Varkuti	Maria
20	Deep neural network-based detection of cerebral aneurysms in MR Angiography	Suprosanna Shit, Augusto Fava Sanches	Dhaval
21	Detection of diabetic retinopathy on fundus images obtained with smartphone camera	Gerome Vivar	Federico
22	Invertible DL	Walter Simson	Fernando
23	Implementation of a Haptic Feedback device	Javier Esteban	Leon
24	Deep learning-based detection and classification of metastases in 3D scans of tumor-bearing mice	Oliver Schoppe	Oliver

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.