- Morphology-Driven Deep Watershed Transform for 3D Tooth Segmentation
- GEPAR3D: Geometry Prior-Assisted Learning for 3D Tooth Segmentation
- Real-time placental vessel segmentation in fetoscopic laser surgery for Twin-to-Twin Transfusion Syndrome
- Segmenting the Inferior Alveolar Canal in CBCTs Volumes: the ToothFairy Challenge
- Let Me DeCode You: Decoder Conditioning with Tabular Data
- POTHER: Patch-Voted Deep Learning-based Chest X-ray Bias Analysis for COVID-19 Detection
Research / Medical Imaging and Robotics
Tomasz Szczepański
PhD Student in Medical Imaging and Robotics
Tomasz Szczepański is a PhD candidate at the Sano Centre for Computational Medicine, where he is a member of the Medical Imaging and Robotics group. His doctoral research, supervised by Prof. Tomasz Trzciński (WUT) and co-supervised by Dr. Arkadiusz Sitek (Harvard Medical School), focuses on medical imaging, multimodal data integration, and geometric approaches to 3D image segmentation and generation.His work has been presented at MICCAI (2023–2025) and ICCS (2022), and published in IEEE Transactions on Medical Imaging and Medical Image Analysis (2025). In 2025 he received an Outstanding Reviewer Award (Honorable Mention) at MICCAI, the only reviewer affiliated with a Polish institution to receive this distinction that year, following his service as a MICCAI reviewer since 2024.He holds an MSc in Computer Science from WUT (2022, summa cum laude), for which his thesis on chest X-ray bias analysis in COVID-19 patients received a distinction in the National Master 4 Science competition, and a BEng in Photonics Engineering and Mechatronics, also from WUT (2018).