A framework for informing segmentation of in vivo MRI with information derived from ex vivo imaging: Application in the medial temporal lobe

Abstract

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.

Fig. 1

Comparison of high-resolution T2-weighted and standard T1-weighted MRI in the same subject. T2-weighted MRI reveals hippocampal layers and better distinguishes between cortical gray matter and meninges.

Fig. 2

Examples of MTL subregion segmentation using ASHS in 3T and 7T in vivo MRI scans. Both examples show the median performance in a set of cross-validation experiments reported in [5] and [7].

Fig. 3

Aligned in vivo 3 tesla and ex vivo 9.4 tesla MRI of the hippocampal head (a–d) and hippocampal body (e–h). The shape of the hippocampus and of white matter band (white arrow in a and b) can be appreciated in both the in vivo and ex vivo images, as well as the endfolial pathway (white arrowhead in e and f), different subicular (black arrowheads in e and f) and perirhinal cortical layers (black arrow in a and b). On the other hand, compared to ex vivo images, on in vivo images CA appears smaller (closed white arrrowheads in c, d, g, h), the hippocampus appears slightly larger (especially c and d) and cysts appear larger (white arrow in a and b).

Fig. 4

3D reconstruction of histological slices into the MRI space. Top row: axial views of reconstructed histology, MRI of 1cm thick tissue slabs, and MRI of intact MTL. Bottom row: corresponding sagittal views, with hippocampal subfield labels overlaid in color.

Fig. 5

Preliminary comparison of automatic segmentation of in vivo MRI to the manual segmentation in histology for the same subject. Ex vivo MRI is used as the intermediate modality to match information between histology and in vivo MRI. It should also be noted that the histology annotation includes more subregions. The blue and light blue in the histology sections are subdivision of the DG and correspond to the dark blue in the MR images, whereas the cyan in the histology sections refers to the strata radiatum and lacunoso-moleculare, which is split between the DG and CA in ASHS.

Fig. 6

Left: sagittal and coronal views of the unbiased hippocampus template obtained by groupwise registration of 25 ex vivo specimen MRI scans. Right: Hippocampal subfield labels, mapped from 10 histology datasets into the corresponding ex vivo MRI scan and then to the template, and averaged in the template space.

Fig. 7

Pilot result illustrating the feasibility of integrating ex vivo data into ASHS. See text for details.