Hippocampal segmentation for brains with extensive atrophy using three-dimensional convolutional neural networks.

Hippocampal volumetry is a critical biomarker of aging and dementia, and it is widely used as a predictor of cognitive performance; however, automated hippocampal segmentation methods are limited because the algorithms are (a) not publicly available, (b) subject to error with significant brain atrophy, cerebrovascular disease and lesions, and/or (c) computationally expensive or require parameter tuning. In this study, we trained a 3D convolutional neural network using 259 bilateral manually delineated segmentations collected from three studies, acquired at multiple sites on different scanners with variable protocols. Our training dataset consisted of elderly cases difficult to segment due to extensive atrophy, vascular disease, and lesions. Our algorithm, (HippMapp3r), was validated against four other publicly available state-of-the-art techniques (HippoDeep, FreeSurfer, SBHV, volBrain, and FIRST). HippMapp3r outperformed the other techniques on all three metrics, generating an average Dice of 0.89 and a correlation coefficient of 0.95. It was two orders of magnitude faster than some of the tested techniques. Further validation was performed on 200 subjects from two other disease populations (frontotemporal dementia and vascular cognitive impairment), highlighting our method's low outlier rate. We finally tested the methods on real and simulated "clinical adversarial" cases to study their robustness to corrupt, low-quality scans. The pipeline and models are available at: https://hippmapp3r.readthedocs.ioto facilitate the study of the hippocampus in large multisite studies.

Magnetoencephalography adaptation reveals depth-cue-invariant object representations in the visual cortex.

Independent of edges and 2-D shape that can be highly informative of object identity, depth cues alone can also give rise to vivid and effective object percepts. The processing of different depth cues engages segregated cortical areas, and an efficient object representation would be one that is invariant to depth cues. Here, we investigated depth-cue invariance of object representations by measuring the category-specific response to faces-the M170 response measured with magnetoencephalography. The M170 response is strongest to faces and is sensitive to adaptation, such that repeated presentation of a face diminishes subsequent M170 responses. We used this feature of the M170 and measured the degree to which the adaptation effect is affected by variations in depth cue and 3-D object shape. Subjects viewed a rapid presentation of two stimuli-an adaptor and a test stimulus. The adaptor was either a face, a chair, or a face-like oval surface, and rendered with a single depth cue (shading, structure from motion, or texture). The test stimulus was always a shaded face of a random identity, thus completely controlling for low-level influences on the M170 response to the test stimulus. In the left fusiform face area, we found strong M170 adaptation when the adaptor was a face regardless of its depth cue. This adaptation was marginal in the right fusiform and negligible in the occipital regions. Our results support the presence of depth-cue-invariant representations in the human visual system, alongside size, position, and viewpoint invariance.

Gaze behavior during 3-D face identification is depth cue invariant.

Gaze behavior during scene and object recognition can highlight the relevant information for a task. For example, salience maps-highlighting regions that have heightened luminance, contrast, color, etc. in a scene-can be used to predict gaze targets. Certain tasks, such as face recognition, result in a typical pattern of fixations on high salience features. While local salience of a 2-D feature may contribute to gaze behavior and object recognition, we are perfectly capable of recognizing objects from 3-D depth cues devoid of meaningful 2-D features. Faces can be recognized from pure texture, binocular disparity, or structure-from-motion displays (Dehmoobadsharifabadi & Farivar, 2016; Farivar, Blanke, & Chaudhuri, 2009; Liu, Collin, Farivar, & Chaudhuri, 2005), and yet these displays are devoid of local salient 2-D features. We therefore sought to determine whether gaze behavior is driven by an underlying 3-D representation that is depth-cue invariant or depth-cue specific. By using a face identification task comprising morphs of 3-D facial surfaces, we were able to measure identification thresholds and thereby equate for task difficulty across different depth cues. We found that gaze behavior for faces defined by shading and texture cues was highly comparable, but we observed some deviations for faces defined by binocular disparity. Interestingly, we found no effect of task difficulty on gaze behavior. The results are discussed in the context of depth-cue invariant representations for facial surfaces, with gaze behavior being constrained by low-level limits of depth extraction from specific cues such as binocular disparity.