Implementing a Pseudo-3D Technique for virtual Dynamic Contrast Enhancement

Artifacts Simulation in CT Images


Computed Tomography (CT) is a powerful imaging modality, but its images often suffer from artifacts that can obscure crucial diagnostic information. Physics-informed artifact simulation offers a promising solution by realistically modeling artifact generation based on underlying physical principles. This approach enables improved artifact understanding, provides realistic training data for machine learning algorithms, and allows for robust evaluation of artifact correction techniques.

This project will focus on exploring state-of-the-art techniques for simulating various types of CT artifacts and investigating their impact on image quality. We will assess the potential of utilizing these simulations to develop advanced artifact reduction methodologies. By further researching this cutting-edge field, we hope to contribute to the continuous improvement of the accuracy and reliability of CT imaging.


  • Completion of Deep Learning is mandatory.
  • Proficiency in PyTorch is essential.
  • Strong analytical and problem-solving skills.

Prospective candidates are warmly invited to send their CV and transcript to

Lightweight Early Forest Fire Detection from Unmanned Aerial Vehicles based on Spatial-Temporal Correlation

PCA Visualization of Foundational Representation Learning in Medical Images

Work description
This project aims to investigate the state-of-the-art foundational representation learning for different medical images, plus, we use PCA to visualize the extracted features.

Good understanding of Deep Learning (advanced DL is better)

Please send your transcript of record with your application.

Evaluation of Reference-Free Registration Methods for Dynamic Vascular Roadmaps

Calving Fronts and How to Segment Them Using Diffusion Networks

Global warming is impacting every part of our planet, and is also responsible for the rise of sea levels
around the world, posing a threat to a majority of the world’s population living in coastal areas. While
there are multiple factors contributing to sea level rise (SLR), such as thermal expansion due to warmer
oceans, it is also in greater part caused by the melting of glaciers and ice regions which stream into
the ocean [1]. It is therefore important for us to understand and monitor glacier ice loss, specifically
for marine- or lake-terminating glaciers. We can do so by looking at calving front movement, where
calving fronts represent the border between an ocean and a glacier. Delineating this exact front position
is fundamental for analysing the health of our glaciers and how global warming is impacting them.
Manually delineating calving fronts is incredibly time intensive, which is why in recent years, researchers
have started automating this process by turning towards deep learning algorithms. Gourmelon et
al. [2] used a U-Net for segmenting SAR images into different regions and then extracted the calving
front in a post-processing step. Wu et al. [3] combined two U-Nets to develop a cross-resolution
segmentation method, which improves the network’s ability to classify the calving front by having
coarse and fine-grained feature maps interact with each other through an attention-based hooking
Diffusion models have made headlines over the past year for their ability to produce fantastically
realistic images [4]. Since the inception of diffusion models, researchers have also started using them
for image segmentation, like in SegDiff [5], which has been further explored in the medical field
with EnsemDiff [6], as well as MedSegDiff and MedSegDiff-V2 [7, 8]. In the field of calving front
delineation however, using diffusion models has not yet been tested, which is what the focus of this
thesis will be.

In detail, the thesis consists of the following parts:
• a literature review of diffusion models being used for image segmentation tasks,
• a review of diffusion models to segment SAR calving front images into different zones,
• using a diffusion model to directly segment calving front positions,
• comparing the created diffusion model against other methods that were evaluated on the CaFFe
dataset [9].


[1] Hans-Otto P¨ortner, Debra C Roberts, Val´erie Masson-Delmotte, Panmao Zhai, Melinda Tignor, Elvira
Poloczanska, and NM Weyer. The ocean and cryosphere in a changing climate. IPCC special report on the
ocean and cryosphere in a changing climate, 1155, 2019.
[2] Nora Gourmelon, Thorsten Seehaus, Matthias Braun, Andreas Maier, and Vincent Christlein. Calving
fronts and where to find them: a benchmark dataset and methodology for automatic glacier calving front
extraction from synthetic aperture radar imagery. Earth System Science Data, 14(9):4287–4313, 2022.
[3] Fei Wu, Nora Gourmelon, Thorsten Seehaus, Jianlin Zhang, Matthias Braun, Andreas Maier, and Vincent
Christlein. Amd-hooknet for glacier front segmentation. IEEE Transactions on Geoscience and Remote
Sensing, 61:1–12, 2023.
[4] Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising diffusion probabilistic models. Advances in neural
information processing systems, 33:6840–6851, 2020.
[5] Tomer Amit, Tal Shaharbany, Eliya Nachmani, and Lior Wolf. Segdiff: Image segmentation with diffusion
probabilistic models. arXiv preprint arXiv:2112.00390, 2021.
[6] Julia Wolleb, Robin Sandk¨uhler, Florentin Bieder, Philippe Valmaggia, and Philippe C Cattin. Diffusion
models for implicit image segmentation ensembles. In International Conference on Medical Imaging with
Deep Learning, pages 1336–1348. PMLR, 2022.
[7] Junde Wu, Huihui Fang, Yu Zhang, Yehui Yang, and Yanwu Xu. Medsegdiff: Medical image segmentation
with diffusion probabilistic model. arXiv preprint arXiv:2211.00611, 2022.
[8] Junde Wu, Rao Fu, Huihui Fang, Yu Zhang, and Yanwu Xu. Medsegdiff-v2: Diffusion based medical
image segmentation with transformer. arXiv preprint arXiv:2301.11798, 2023.
[9] Nora Gourmelon, Thorsten Seehaus, Julian Klink, Matthias Braun, Andreas Maier, and Vincent Christlein.
Caffe-a benchmark dataset for glacier calving front extraction from synthetic aperture radar imagery. In
IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, pages 896–898. IEEE,
[10] Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming
Lin, Alban Desmaison, Luca Antiga, and Adam Lerer. Automatic differentiation in PyTorch. In NIPS
Autodiff Workshop, 2017.

Enhanced Peer Review Analysis using transformer based sentiment distillation

This thesis investigates the utilization of transformer-based models to optimize the automation and accuracy of peer review analysis and decision-making in scholarly publishing. By integrating cutting-edge techniques, we have designed prompt to extract influential sections from peer reviews and the implementation of sentiment analysis to forecast paper acceptance determinations. The methodology encompasses a meticulous process of generating a “Critique Summary” that distills the sentimental polarity with influential excerpts, incorporating both review ratings and textual content. Subsequently, a state-of-the-art transformer model, such as Llama 2, undergoes fine-tuning using the synthesized dataset to refine its predictive capabilities for paper acceptance decisions. Through this innovative approach, we have tried to streamline the peer review workflow, markedly diminishing the duration between initial reviews and final editorial decisions, thus enhancing the overall efficacy and efficiency of academic publishing endeavors.

Evaluation of the novel class of promptable image segmentation foundation models for radiotherapy tumor autosegmentation

ISLES Challenge 2024: Infarct segmentation from CT images

Final, post-treatment infarct segmentation from pre-treatment acute imaging (CT) and clinical data.

Idea: Investigate data from this year’s ISLES 2024 challenge and build + train a model for stroke lesion segmentation. Potentially submit the model to the challenge.