Bjoern Menze (TUM), Marie Piraud (TUM), and external lecturers.
Type: Master Seminar Module IN2107
SWS: 2+0
ECTS: 5 Credits
Location: IMETUM Seminar Room
Time: Wednesday 1300-1500
Course Language: English
Many clinical imaging sequences include image modalities that provide unique insights into the human body: describing blood flow pattern or water diffusion, visualizing micro-structural tissue properties, or quantifying metabolic processes that have been found to be relevant for specific diseases. To access and explore the rich biological information of such multimodal CT, PET, and MR image data sets, different approaches from biophysical modeling and computational physiology are used.
The topic of this seminar is to survey such computational models by reading, discussing and understanding key publications in this field, for example, on:
Emphasis is put on improving presentation style. All presenters will present their talk twice during the course of the semester, and after the first time the group discussion will focus on presentation style.
Each presentation is followed by a discussion and everyone is encouraged to actively participate. The handout must include all references used and should be your own review of the paper (don't copy & paste). Both handout and presentation will have to be in English. It can follow the structure of the presentation: Intro (incl. statement of the problem, prior work, contribution of the present work), Methods (previously existing and improvements), Experiments (data and results), Conclusion / Summary.
The seminar consists of an introductory meeting and seminar sessions. Each of the sessions will consist of several student presentations. All seminar participants are expected to be present at every session and to actively participate in the discussion.
The papers that we are going to discuss in the seminar belong to four different research areas:
Area | Description | Presenter | Tutor |
---|---|---|---|
Models & uncertainty | Tanno et al. Bayesian Image Quality Transfer with CNNs: Exploring Uncertainty in dMRI Super-Resolution. Miccai 2017 | Kim | Gomez |
Owen et al. Anatomy-driven modelling of spatial correlation for regularisation of arterial spin labelling images. Miccai 2017 | Bartol | Ulas | |
Dhamala et al. Quantifying the uncertainty in model parameters using Gaussian process based Markov chain Monte Carlo: an application to cardiac electrophysiological models. Miccai 2017l | Aschenbrenner | Zimmermann | |
Anatomical atlases & populations | Arnaudon et al. A Stochastic large deformation model for computational anatomy. Ipmi 2017 | ||
Mirikharaji et al. Globally-optimal anatomical tree extraction from 3D medical images using pictorial structures and minimal paths. Miccai 2017 | |||
Chen et al. The active brain atlas: combining 3D anatomical models with texture detectors. Miccai 2017 | Boghiu | Alberts | |
Dalca et al. Population based image imputation. Ipmi 2017 | El Husseini | Sekuboyina | |
Kong et al. Cancer metastasis detection via spatially structured deep network. Ipmi 2017 | Kayalıbay | Piraud | |
Heinrich et al. BRIEFnet: Deep Pancreas Segmentation Using Binary Sparse Convolutions. Miccai 2017 | |||
Heinrich et al. Multi-Organ Segmentation using Vantage Point Forests and Binary Context Features. Miccai 2016 | |||
Vessels & networks | Moriconi et al. VTrails: inferring vessels with geodesic connectivity trees. Ipmi 2017 | ||
Matejek et al. Compresso: Efficient compression of segmentation data for connectomics. Miccai 2017 | |||
Wang et al. Automatic labeling of vascular structures with topological constraints via HMM. Miccai 2017 | Fischer | Wels | |
Rempfler et al. Cell Lineage tracing in lens-free microscopy videos. Miccai 2017 | Ebert | Rempfler | |
Disease modeling | Venkatraghavan et al. A discriminative event based model for Alzheimer's disease progression modeling. Ipmi 2017 | Nagy | Piraud |
Elbaz et al. Hierarchical Shape Distributions for Automatic Identification of 3D Diastolic Vortex Rings from 4D Flow MRI. Miccai 2015 | |||
Marinescu et al. A vertex clustering model for disease progression: application to cortical thickness images. Ipmi 2017 | Eisawy | Shi | |
Chang et al. Personalized pancreatic tumor growth prediction via group learning. Miccai 2017 | |||
Parisot et al. Spectral graph convolutions on population graphs for disease prediction. Miccai 2017 | Andriamanga | Shi | |
Giroux et al. Biomechanical Patient-specific model of the respiratory system based on 4D CT scans and controlled by personalized physiological compliance. Miccai 2017 | |||
Radiomic biomarkers | Schlegl et al. Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. Ipmi 2017 | Einarsson | Tetteh |
Doyle et al. Predicting future disease activity and treatment responders for Multiple Sclerosis patients using a bag-of-lesions brain representation. Miccai 2017 | Pita | Alberts | |
Yao et al. Deep correlational learning for survival prediction from multi-modality data. Miccai 2017 | |||
Antunes et al. Radiomic spatial textai descriptor (RADISTAT): Characterizing intra-tumoral heterogeneity for response & outcome prediction. Miccai 2017 | |||
Chen et al. Multi-label inductive matrix completion for joint MGMT and IDH1 status prediction for glioma patients. Miccai 2017 |
The seminar consists of an introductory meeting and seminar sessions. Each of the sessions will consist of several student presentations. All seminar participants are expected to be present at every session and to actively participate in the discussion.
A lot of scientific publications can be found online.
The following list may help you to find some further information on your particular topic:
Some publishers:
Libraries (online and offline):
Some further hints for working with references:
If you find useful resources that are not already listed here, please tell us, so we can add them for others. Thanks.
TeachingForm | |
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Title: | Image based biomedical modeling seminar |
Professor: | Bjoern Menze |
Tutors: | Marie Piraud |
Type: | Hauptseminar |
Information: | Hauptseminar, SWS: 2, ECTS: 5 |
Term: | 2017WiSe |
Abstract: |