Segmentation, visualization and analysis of large multimodal clinical image - MISS 2014 (03)

Prof. Polina Golland (Massachusetts Institute of Technology, US) was one of the speakers of Medical Imaging Summer School (Sicily, July 28-August 1, 2014), here you can find her lecture 2: "Segmentation, visualization and analysis of large multimodal clinical image".

Abstract. We present an analysis framework for large studies of multimodal clinical quality brain image collections. 

Processing such datasets is challenging due to low resolution, poor contrast, misaligned images, and restricted field of view. We adapt existing registration and segmentation methods and build a computational pipeline for spatial normalization and feature extraction. 

The resulting aligned dataset enables clinically meaningful analysis of spatial distributions of relevant anatomical features and of their evolution with age and disease progression. We demonstrate the approach on a neuroimaging study of stroke with more than 1,000 patients across multiple imaging sites. 

We show that by combining data from several modalities, we can automatically segment important biomarkers such as white matter hyperintensity and characterize pathology evolution in this heterogeneous cohort. Specifically, we examine two sub-populations with different dynamics of white matter hyperintensity changes as a function of age.

Shooting: Lorenzo Di Silvestro, Mauro Sodano
Editing and post-production: Lorenzo Di Silvestro, Agata Ventura