Testing a convolutional neural network-based hippocampal segmentation method in a stroke population

Abstract

As stroke mortality rates decrease, there has been a surge of effort to study post-stroke dementia (PSD) to improve long-term quality of life for stroke survivors. Hippocampal volume may be an important neuroimaging biomarker in post-stroke dementia, as it has been associated with many other forms of dementia. However, studying hippocampal volume using MRI requires hippocampal segmentation. Advances in automated segmentation methods have allowed for studying the hippocampus on a large scale, which is important for robust results in the heterogeneous stroke population. However, most of these automated methods use a single atlas-based approach and may fail in the presence of severe structural abnormalities common in stroke. Hippodeep, a new convolutional neural network-based hippocampal segmentation method, does not rely solely on a single atlas-based approach and thus may be better suited for stroke populations. Here, we compared quality control and the accuracy of segmentations generated by Hippodeep and two well-accepted hippocampal segmentation methods on stroke MRIs (FreeSurfer 6.0 whole hippocampus and FreeSurfer 6.0 sum of hippocampal subfields). Quality control was performed using a stringent protocol for visual inspection of the segmentations, and accuracy was measured as volumetric and spatial comparisons to the manual segmentations. Hippodeep performed significantly better than both FreeSurfer methods in terms of quality control and spatial accuracy. Overall, this study suggests that both Hippodeep and FreeSurfer may be useful for hippocampal segmentation in stroke rehabilitation research, but Hippodeep may be more robust to stroke lesion anatomy.

Cortical thickness estimation in individuals with cerebral small vessel disease, focal atrophy, and chronic stroke lesions

“…While small acute strokes may have minimal effects on tissue segmentation, large chronic cortico-subcortical stroke lesions introduce alterations to brain morphometry resulting in failed segmentation in most brain segmentation algorithms (43,(45)(46)(47). Although this issue is particularly relevant in individuals with CVD, cerebral small vessel disease and brain atrophy that are commonly observed in patients with Alzheimer's and other related dementias present similar challenges when estimating cortical thickness.…”

Cortical Thickness Estimation in Individuals With Cerebral Small Vessel Disease, Focal Atrophy, and Chronic Stroke Lesions

“…While small acute strokes may have minimal effects on tissue segmentation, large chronic cortico-subcortical stroke lesions introduce alterations to brain morphometry resulting in failed segmentation in most brain segmentation algorithms (Wang et al, 2012;Yang et al, 2016;Siegel et al, 2017;Zavaliangos-Petropulu et al, 2020). Although this issue is particularly relevant in individuals with CVD, cerebral small vessel disease and brain atrophy that are commonly observed in patients with Alzheimer's and other related dementias present similar challenges when estimating cortical thickness.…”

A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms

“…The reach of the ATLAS v1.2 dataset has also extended beyond stroke lesion segmentation. It has also been used as a key example of a large, public neuroimaging dataset, 29 to provide published guidelines on how to perform lesion segmentation, 30 to evaluate the performance of different hippocampal segmentation methods in stroke, 31 to test other non-stroke automated methods, such as anomaly 32 and asymmetry detection, 33 and as inspiration for future AI programs and large public datasets, 34 among other uses. It is a valuable educational resource and has been used as a teaching resource in courses on machine learning and computer vision as well as for student thesis projects.…”