A framework for informing segmentation of in vivo MRI with information derived from ex vivo imaging: Application in the medial temporal lobe
Paul A. Yushkevich, Laura E.M. Wisse, Daniel H. Adler, Ranjit Ittyerah, John B. Pluta, John L. Robinson, Theresa Schuck, John Q. Trojanowski, Murray Grossman, John A. Detre, Mark A. Elliott, Jon B. Toledo, Weixia Liu, Stephen Pickup, Sandhitsu R. Das, David A. Wolk · 2016
Automatic segmentation of cortical and subcortical structures is commonplace in brain MRI literature and is frequently used as the first step towards quantitative analysis of structural and functional neuroimaging. Most approaches to brain structure segmentation are based on propagation of anatomical information from example MRI datasets, called atlases or templates, that are manually labeled by experts. The accuracy of automatic segmentation is usually validated against the "bronze" standard of manual segmentation of test MRI datasets. However, good performance vis-a-vis manual segmentation does not imply accuracy relative to the underlying true anatomical boundaries. In the context of segmentation of hippocampal subfields and functionally related medial temporal lobe cortical subregions, we explore the challenges associated with validating existing automatic segmentation techniques against underlying histologically-derived anatomical "gold" standard; and, further, developing automatic in vivo MRI segmentation techniques informed by histological imaging.