BSI whole‐brain volume change estimation initialised with fully automatic LEAP segmentation: Neuroimaging / New imaging methods. (7th December 2020)

• Record Type: Journal Article
• Authors: Joules, Richard; Wolz, Robin
• Is Part Of: Alzheimer's & dementia. Volume 16(2020)Supplement 4
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-12-07
• Subjects: Alzheimer's disease -- Periodicals; Alzheimer Disease -- Periodicals; Dementia -- Periodicals; Démence; Maladie d'Alzheimer; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.83
• DOI: 10.1002/alz.044795
• Languages: English
• ISSNs: 1552-5260

Abstract

Abstract: Background: Whole brain atrophy as measured from T1‐wieghted MR images is a key biomarker for progression in Alzheimer's disease (AD) and employed as marker for treatment efficacy in clinical trials. The Boundary Shift Integral (BSI) methodology (Freborough;IEEE;1997) is the widely established standard and well validated method for volume change. BSI requires initialisation with a whole‐brain segmentation at both timepoints, manual segmentations remain the gold standard, however the BSI approach is robust to minor segmentation, potentially facilitating the use of automatic segmentation approaches. Furthermore, to improve sensitivity to volume change and exploit the probabilistic nature of many automatic segmentations a generalised form of the BSI (gBSI) pipeline has been proposed (Prados;NeurobiolAging;2015). The use of a fully automatic pipeline to estimate volume change provides a scalable solution for clinical trial deployment without the burden of manual segmentation or edits and associated variability introduced. Here we assess the use of LEAP (Ledig;ProcISBI;2012), a fully automatic whole brain segmentation tool providing both binary and probabilistic parcellations, to initialise the BSI, as implemented in MIDAS, and gBSI pipelines. Method: Whole brain volume change over 1 year was estimated for 48 participants, acquired as part of a clinical trial, with the BSI and gBSI methods initialised with 1) manually generated whole brain segmentations and 2) automatically estimated whole brain segmentations generate with LEAP. No manual corrections were performed; all LEAP segmentations passed a visual quality control inspection. Result: Figure 1 shows the relationship between the manually initialised BSI and the fully‐automated LEAP‐BSI approach. Table 1 shows average measures of volume change across all tested methods. All measures were significantly correlated with non‐significant differences in reported mean volume change. Conclusion: LEAP offers segmentations suitable for initialisation of the BSI method providing a pipeline for fully automatic volume estimation consistent with the semi‐automated BSI approach, providing a scalable solution for clinical trial deployment without the associated variability and resource burden required for fully manual segmentations. Furthermore the probabilistic output of LEAP allows for the use of the gBSI methodology with the associated potential of increased sensitivity to volume change.