Advanced Image Analysis and Quantification of Clinical Pulmonary Optical Endomicroscopy
The dynamic and large datasets that are collected as part of pulmonary endomicroscopy procedures are challenging to analyse and also quantify. This project will deal with the development and deployment of image computing tools to objectively quantify fluorescence levels and imaging patterns in data generated through clinical trials. The student will work with both the clinical team (Dhaliwal) at Edinburgh and the medical image computing group at UCL (Vercauteren) to develop novel and clinically deployable methods to aid clinical decision making and analysis.
The clinical data will be derived from optical molecular imaging agents that are being applied and delivered to patients with lung injury and critical infections in intensive care with additional benefits seen in the field of lung cancer.
The image computing challenges include the calibration and unmixing of multiparametric data in time and space as a first step, and the development of assessment methods leveraging a combination of quantitative fluorescence and machine learning. The student will work with clinical data throughout the project and will be heavily involved in ongoing clinical trials as an observer, so that he/she fully appreciates the data aquistion and the clinical challenges and also the clinical pull.
Optical molecular imaging in the lung is an emerging sensing and imaging modality. While its potential is now recognised, similar to many novel interventional imaging modalities, it poses new challenges in terms of intra-procedure image interpretation. As clinical data is received, it is vital that novel algorithms are developed that are able to analyse the data in real-time to support the clinical decision process. Furthermore, data-driven algorithms have the potential to identify new disease stratifications. This could play an important role to realise the clinical benefits that are expected to follow the in-human adoption of the technology. This collaboration is between the leading UK pioneers of optical pulmonary imaging alongside the UKs most established and best medical image computing department at UCL.