Designing fast and robust algorithms for medical image processing

Wednesday, November 16, 2011 - 11:30am - 12:30pm
Keller 3-180
Elsa Angelini (Telecom ParisTech)
Quantification from medical images involves three levels of developments:
- Modeling of the organs
- Extraction of the visual features
- Formulation of the quantification task
Regarding organ modeling, geometric encoding of the shape is designed as a tradeoff between flexibility and robustness. Encoding of the variability within a population is a complex task that can have drawbacks when handling pathological cases. On the other hand, generic anatomical knowledge, especially regarding the context of the organ, can provide rich and more robust information, with spatial relations for example.
Regarding the visual features, images are richer than they appear in terms of tissue signature, embedding multiscale information. The field of image processing has evolved slowly in the design of sophisticated organ-specific visual features, the majority of them remaining very basic. Future challenges remain open regarding the need to correlate multi-modal tissue signatures with physiological characteristics.
Formulation of the quantification task such as segmentation, tracking or detection of longitudinal changes can be formulated either with a deterministic or stochastic formalism. Algorithms remain poorly robust to image quality, lack of image calibration, parameter tuning and presence of pathologies. Finer interactions between algorithmic tuning and image content and better calibration of image content is currently under investigation to address this lack of robustness and reproducibility.

These three components of the pipeline will be discussed, with illustrations on brain, cardiac liver and obstetric data. Emphasis will be paid to the constraints of being fast and robust, in the context of handling large data sets with great variability and pathologies.