https://github.com/rasmusrpaulsen/dtu-u-net-course
Three weeks course focusing on getting a U-net up and running
https://github.com/rasmusrpaulsen/dtu-u-net-course
Last synced: 6 months ago
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Three weeks course focusing on getting a U-net up and running
- Host: GitHub
- URL: https://github.com/rasmusrpaulsen/dtu-u-net-course
- Owner: RasmusRPaulsen
- Created: 2022-12-22T09:33:55.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-30T15:00:28.000Z (over 2 years ago)
- Last Synced: 2025-02-04T16:50:00.859Z (8 months ago)
- Language: Python
- Size: 13.7 KB
- Stars: 0
- Watchers: 1
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# DTU-U-net-course
**Special course in deep learning for medical image segmentation**
- 5-ECTS
- 3-Weeks
## Course description
The aim of this course is to implements, test and evaluate a complete software framework for segmenting anatomical structures in 3D medical scans.
The data used for this project is a public data set of 3D computed tomography cardiac scans with ground truth anatomical annotations (MM-WHS). Alternatively, a data set containing abdominal structures can be used.
## Learning objectives
After the course, the student can:
- Describe the nature of 3D computed tomography scans including spatial resolution, inter-slice distance and Hounsfield units.
- Describe the concept of anatomical annotations
- Use 3D slicer to visualize 3D medical data including annotations and segmentation results
- Describe the U-net deep learning architecture including convolution and pooling
- Describe loss functions including mean squared error
- Implement a basic 2D U-net architecture in Pytorch
- Transfer code and data to the DTU Compute GPU cluster or to a dedicated GPU server
- Train and test a deep learning algorithm on the DTU Compute GPU cluster or on a dedicated GPU server
- Test a 2D U-net on an independent test set
- Compare the output of a 2D U-net with ground truth annotations using the DICE similarity measure
- Evaluate the quality of a segmentation using visual inspection and determine if the segmentation is anatomically plausible.
## Course material
- Selected material from **02456schedule**: https://docs.google.com/document/d/e/2PACX-1vRL1M_zEyzH8d4jKHltDauAYIPgtudXV0uRwTspy7i1mt2WcMaUms1H2RBcANxfFKfiMm4BbJ5cYL9C/pub
- Selected material from https://www.deeplearningbook.org/
- **U-net paper**: [U-net: Convolutional networks for biomedical image segmentation](https://arxiv.org/abs/1505.04597)
- **GPU Cluster Wiki**: https://itswiki.compute.dtu.dk/index.php/GPU_Cluster
## Teaching and supervision
The course is to some degree a group based self-study course where the supervisor will have daily meetings with the students. There will also be a teaching assistant associated to the course.
## Evaluation:
7-grade scale based on written report of approximately 15 pages written by the student group.
## Schedule 2023:
- **Week 1**: January 9. – 13.
- **Week 2**: January 16. – 20.
- Holidays: January 23. – 27.
- **Week 3**: January 30. – February 3.
## Preparations:
- Have a Python / Anaconda environment up and running.
- Install and learn to use a code editor. Recommended Visual Studio Code.
- Do the following exercise:
- https://github.com/RasmusRPaulsen/DTUImageAnalysis/tree/main/exercises/ex1-IntroductionToImageAnalysis
## Data:
- [MM-WHS: Multi-Modality Whole Heart Segmentation](https://zmiclab.github.io/zxh/0/mmwhs/) . Rasmus has these data.
- [TotalSegmentator dataset](https://zenodo.org/record/6802614#.Y6Qn23bMIR8)
### Week 1:
- Monday : Week 1-2 from 02456schedule
- Tuesday : Week 1-2 from 02456schedule
- Wednesday: Visit to Rigshospitalet. [3D Slicer](https://www.slicer.org/) on abdominal data / heart data. Try [TotalSegmentator](https://www.youtube.com/watch?v=osvMB5SKcVQ).
- Thursday: Week 2-3 from 02456schedule
- Friday: Week 2-3 from 02456schedule
### Week 2:
- Monday : GPU Cluster intro. Week 2-3 from 02456schedule
- Tuesday : Week 2-3 from 02456schedule
- Wednesday: U-Net intro. Week 2-3 from 02456schedule
- Thursday: Week 3-4 from 02456schedule
- Friday: Week 3-4 from 02456schedule
### Week 3:
- Monday : U-net startup. Data preparation
- Tuesday : U-net implementation, training, validation and testing
- Wednesday: U-net implementation, training, validation and testing
- Thursday: U-net implementation, training, validation and testing
- Friday: U-net implementation, training, validation and testing
## Links and other material
- [3Blue1Brown on Neural networks](https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi)