Computational Medical Imaging (Lab)

Lab: Computational Medical Imaging (CoMI)

• Lecturers: Prof. Dr. Shadi Albarqouni
• Tutors:
• Module: MA-INF 2230
• Credits: 4 SWS, 9 CP (270h workload)
• Programme: M.Sc. Computer Science
• Mode: Optional (2. or 3. Semester)
• Language: English
• Format: Lab (Praktikum), groups of max. 3 students
• Frequency: Every year

Description

The Computational Medical Imaging (CoMI) Lab is a project-based course in which students carry out a semester-long research project in computational medical imaging. Working in small groups, students identify a well-defined problem, collect or are provided a suitable dataset, implement baseline and novel approaches, and evaluate their results both quantitatively and qualitatively.

Possible project topics (chosen or proposed by the instructors) include:

• Anatomical Structure Segmentation (e.g., multi-atlas, CNN-based)
• Deformable Registration (e.g., classic demons, learning-based registration)
• Lesion/Anomaly Detection (e.g., out-of-distribution detection in X-ray)
• Radiomics & Quantitative Biomarker Extraction (e.g., texture features, deep radiomics)
• Uncertainty Estimation in Deep Learning (e.g., MC-Dropout, Bayesian networks)
• Causality & Bias Analysis in Medical Datasets (e.g., domain shift, fairness)

Learning Goals

Technical Skills

• Acquire a deep understanding of a selected problem in computational medical imaging: from problem identification to data processing, algorithm design, implementation, and evaluation.
• Implement, test, and validate state-of-the-art image-analysis algorithms (e.g., segmentation, registration, reconstruction, classification) on real medical data.
• Develop proficiency in using libraries and toolkits commonly used in medical imaging (e.g., ITK, SimpleITK, PyTorch for deep learning, nibabel/dipy).
• Learn to document software systematically (API documentation, code comments, unit tests) and to generate reproducible experiments (version control, containerization).

Soft Skills

• Collaborate effectively in small groups over the entire semester, dividing tasks (e.g., data curation, algorithmic implementation, evaluation).
• Prepare clear, concise technical documentation and design rationales.
• Present project progress and final results in oral form (midterm demo, final demo).
• Critically assess one’s own results and position them within the state-of-the-art in computational medical imaging.

🗓 Project (Summer 2026)

Segmentation-Aware Benchmarking of Clinical, Radiomic, Deep-Learning and Foundation-Model Biomarkers for NSCLC Survival Prediction on Pretreatment CT

Can AI support reproducible imaging-biomarker discovery for non-small cell lung cancer (NSCLC)? Students benchmark zero-shot and promptable tumour-segmentation models, clinical risk models, handcrafted radiomics and pretrained CT foundation-model representations for prognosis and phenotype discovery. The central question: do prognostic conclusions change when the tumour contour comes from an automated model instead of an expert?

• Dataset: TCIA NSCLC-Radiomics / LUNG1 — pretreatment thoracic CT of 422 patients with manual 3D gross-tumour-volume contours and survival data (doi:10.7937/K9/TCIA.2015.PF0M9REI), the cohort of the landmark radiomics study by Aerts et al.
• Team: 4 students; all share data handling and phenotyping, each leads one experimental work package.
• Software: all experiments run on the lab’s open CoMI library for consistent pre-processing, metrics and figures.

Learning Format

Students work in groups of up to 3 on a semester-long project. Each group will:

• Identify a well-defined problem and collect or be provided a suitable dataset (publicly available or hospital data under ethics approval).
• Perform a literature review to select baseline methods.
• Implement baseline and at least one novel or improved approach.
• Evaluate quantitatively (e.g., Dice, Hausdorff, MSE, classification accuracy) and qualitatively (expert review where possible).
• Document the software (README, API docs) and provide a reproducible environment (Docker/Singularity).
• Present midterm progress (oral demonstration + short written summary) and final results (oral + poster or full report).

Assessment

Graded Exam:
Oral presentation (final demo + Q&A, 20 minutes per group). Written report (12–16 pages, including background, methods, results, discussion).

Ungraded Coursework (required for admission to the exam):
Project work; attendance in course sessions in accordance with the exam regulations of 2023, § 12(6).

Recommended Prerequisites

At least one of the following:

• MA-INF 2222 – Visual Data Analysis
• MA-INF 2312 – Image Acquisition and Analysis in Neuroscience
• MA-INF 2220 – Lab Visualization and Medical Image Analysis (or equivalent)

A solid background in Python programming (and/or C++), linear algebra, calculus, probability theory, and numerical algorithms is required. Familiarity with deep learning frameworks is highly recommended.

References and Resources

Relevant Journals:

• Nature Machine Intelligence
• Medical Image Analysis (MedIA)
• IEEE Transactions on Medical Imaging (TMI)
• Radiology: AI, European Radiology, IEEE Journal of Biomedical and Health Informatics

Relevant Conferences:

• MICCAI (Medical Image Computing & Computer-Assisted Intervention)
• CVPR (IEEE/CVF Computer Vision and Pattern Recognition)
• MIDL (Medical Imaging with Deep Learning)
• ISBI (International Symposium on Biomedical Imaging)
• RSNA (Radiological Society of North America Annual Meeting)

Registration

Registration takes place via eCampus or by contacting Prof. Dr. Shadi Albarqouni.