Single Photon Emission Computed Tomography combined with Computed Tomography (SPECT/CT) provides crucial functional and anatomical data for the assessment of orthopaedic pathologies. However, interpretation of these scans remains largely qualitative and observer-dependent. Recent work has proposed standardized, quantitative analysis pipelines, yet most approaches rely on predefined thresholds or regions of interest. This project seeks to develop an unsupervised learning pipeline capable of automatically identifying and quantifying abnormal uptake regions ("hotspots") in 3D SPECT/CT volumes. The model will extract clinically relevant statistics—such as volume, extent, intensity peak, swelling patterns, and signal variation (e.g., standard deviation)—to support more objective and reproducible diagnostic workflows.

The project will be conducted in close collaboration with the Department of Orthopaedics and Sports Orthopaedics at the University Hospital, the Institute for AI and Informatics in Medicine, and the Universitäre Klinik Orthopädie & Traumatologie, Kantonsspital Baselland. It will be supervised within the AI in Orthopaedics (AIO) group, an interdisciplinary team of clinicians and computer scientists affiliated with these institutions.

- Design and implement an unsupervised 3D clustering algorithm (e.g., DBSCAN, Gaussian Mixture Models) to detect abnormal SPECT/CT hotspots.
- Develop a post-processing pipeline to quantify volumetric and statistical properties of the hotspots, including volume, swelling, standard deviation, and peak uptake.
- Validate the method against clinical annotations and evaluate robustness across anatomical regions and patient cohorts.
- Document findings in a reproducible format and prepare a scientific report or publication.

- Access to a curated dataset of clinical SPECT/CT scans with expert annotations.
- Guidance from both clinical and AI experts in a collaborative, interdisciplinary research setting.
- Opportunity to contribute to ongoing research and potential publication in medical imaging or orthopaedic AI journals.
- Exposure to cutting-edge AI tools and frameworks for 3D medical image analysis.

The student will first explore the landscape of unsupervised clustering techniques in volumetric data and identify suitable algorithms for SPECT/CT intensity distributions. The core task involves implementing a pipeline that segments pathological hotspots without prior labels and calculates clinically relevant statistics. Evaluation will be done using available ground truth or expert-derived annotations, with attention to generalizability and interpretability of the results.

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4. Chen, J., Li, Y., Luna, L.P., Chung, H.W., Rowe, S.P., Du, Y., Solnes, L.B. and Frey, E.C., 2021. Learning fuzzy clustering for SPECT/CT segmentation via convolutional neural networks. Medical physics, 48(7), pp.3860-3877.