Background

Medical image segmentation is a crucial step in many clinical applications, including the body composition analysis (BCA) in radiology for prognosis purposes or in radiation therapy necessary for treatment planning.

Deep learning (DL) has shown remarkable success in medical image segmentation due to its ability to learn complex features from large datasets while being fast and accurate. However, with the growing number of DL-based segmentation algorithms [1,2], it becomes imperative to evaluate and compare their performance to determine their effectiveness in different clinical scenarios.
Goals

Conduct a comprehensive literature review on deep learning-based medical image segmentation algorithms.

Implement/improve an in-house model and evaluate selected algorithms on a dedicated dataset, including CT and anatomical structures associated.

Quantitatively compare the performance of different algorithms using metrics such as Dice similarity coefficient, Jaccard index and BCA metrics.

Prerequisites

Proficiency in programming languages such as Python and deep learning libraries such as TensorFlow or PyTorch.

Ability to critically analyze and interpret research papers related to medical image segmentation.

Contact person

For more information please contact : olivier.debeir@ulb.be, kevin.brouboni@hubruxelles.be
references

[1] Wasserthal, Jakob, et al. "TotalSegmentator: robust segmentation of 104 anatomical structures in CT images." arXiv preprint arXiv:2208.05868 (2022).

[2] Sundar, Lalith Kumar Shiyam, et al. "Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence." Journal of Nuclear Medicine 63.12 (2022): 1941-1948.