A Deep Learning Model for Automated Segmentation of Geographic Atrophy Imaged Using Swept-Source OCT.

PURPOSE: To present a deep learning algorithm for segmentation of geographic atrophy (GA) using en face swept-source OCT (SS-OCT) images that is accurate and reproducible for the assessment of GA growth over time. DESIGN: Retrospective review of images obtained as part of a prospective natural history study. SUBJECTS: Patients with GA (n = 90), patients with early or intermediate age-related macular degeneration (n = 32), and healthy controls (n = 16). METHODS: An automated algorithm using scan volume data to generate 3 image inputs characterizing the main OCT features of GA-hypertransmission in subretinal pigment epithelium (sub-RPE) slab, regions of RPE loss, and loss of retinal thickness-was trained using 126 images (93 with GA and 33 without GA, from the same number of eyes) using a fivefold cross-validation method and data augmentation techniques. It was tested in an independent set of one hundred eighty 6 × 6-mm2 macular SS-OCT scans consisting of 3 repeated scans of 30 eyes with GA at baseline and follow-up as well as 45 images obtained from 42 eyes without GA. MAIN OUTCOME MEASURES: The GA area, enlargement rate of GA area, square root of GA area, and square root of the enlargement rate of GA area measurements were calculated using the automated algorithm and compared with ground truth calculations performed by 2 manual graders. The repeatability of these measurements was determined using intraclass coefficients (ICCs). RESULTS: There were no significant differences in the GA areas, enlargement rates of GA area, square roots of GA area, and square roots of the enlargement rates of GA area between the graders and the automated algorithm. The algorithm showed high repeatability, with ICCs of 0.99 and 0.94 for the GA area measurements and the enlargement rates of GA area, respectively. The repeatability limit for the GA area measurements made by grader 1, grader 2, and the automated algorithm was 0.28, 0.33, and 0.92 mm2, respectively. CONCLUSIONS: When compared with manual methods, this proposed deep learning-based automated algorithm for GA segmentation using en face SS-OCT images was able to accurately delineate GA and produce reproducible measurements of the enlargement rates of GA.

Expression of emotion in the kinematics of locomotion.

Here, we examine how different emotions-happiness, fear, sadness and anger-affect the kinematics of locomotion. We focus on a compact representation of locomotion properties using the intersegmental law of coordination (Borghese et al. in J Physiol 494(3):863-879, 1996), which states that, during the gait cycle of human locomotion, the elevation angles of the thigh, shank and foot do not evolve independently of each other but form a planar pattern of co-variation. This phenomenon is highly robust and has been extensively studied. The orientation of the plane has been correlated with changes in the speed of locomotion and with reduction in energy expenditure as speed increases. An analytical model explaining the conditions underlying the emergence of this plane and predicting its orientation reveals that it suffices to examine the amplitudes of the elevation angles of the different segments along with the phase shifts between them (Barliya et al. in Exp Brain Res 193:371-385, 2009). We thus investigated the influence of different emotions on the parameters directly determining the orientation of the intersegmental plane and on the angular rotation profiles of the leg segments, examining both the effect of changes in walking speed and effects independent of speed. Subjects were professional actors and naïve subjects with no training in acting. As expected, emotions were found to strongly affect the kinematics of locomotion, particularly walking speed. The intersegmental coordination patterns revealed that emotional expression caused additional modifications to the locomotion patterns that could not be explained solely by a change in speed. For all emotions except sadness, the amplitude of thigh elevation angles changed from those in neutral locomotion. The intersegmental plane was also differently oriented, especially during anger. We suggest that, while speed is the dominant variable allowing discrimination between different emotional gaits, emotion can be reliably recognized in locomotion only when speed is considered together with these kinematic changes.

Critical features for the perception of emotion from gait.

Human observers readily recognize emotions expressed in body movement. Their perceptual judgments are based on simple movement features, such as overall speed, but also on more intricate posture and dynamic cues. The systematic analysis of such features is complicated due to the difficulty of considering the large number of potentially relevant kinematic and dynamic parameters. To identify emotion-specific features we motion-captured the neutral and emotionally expressive (anger, happiness, sadness, fear) gaits of 25 individuals. Body posture was characterized by average flexion angles, and a low-dimensional parameterization of the spatio-temporal structure of joint trajectories was obtained by approximation with a nonlinear mixture model. Applying sparse regression, we extracted critical emotion-specific posture and movement features, which typically depended only on a small number of joints. The features we extracted from the motor behavior closely resembled features that were critical for the perception of emotion from gait, determined by a statistical analysis of classification and rating judgments of 21 observers presented with avatars animated with the recorded movements. The perceptual relevance of these features was further supported by another experiment showing that artificial walkers containing only the critical features induced high-level after-effects matching those induced by adaptation with natural emotional walkers.

An analytical formulation of the law of intersegmental coordination during human locomotion.

The law of intersegmental coordination is a kinematic law that describes the coordination patterns among the elevation angles of the lower limb segments during locomotion (Borghese et al. in J Physiol 494:863-879, 1996). This coordination pattern reduces the number of degrees of freedom of the lower limb to two, i.e. the elevation angles covary along a plane in angular space. The properties of the plane that constrains the time course of the elevation angles have been extensively studied, and its orientation was found to be correlated with gait velocity and energy expenditure (Bianchi et al. in J Neurophysiol 79:2155-2170, 1998). Here, we present a mathematical model that represents the rotations of the elevation angles in terms of simple oscillators with appropriate phase shifts between them. The model explains what requirements the time courses of the elevation angles must fulfill in order for the angular covariation relationship to be planar. Moreover, an analytical formulation is proposed for both the orientation of the plane and for the eccentricity of the nearly elliptical shape that is generated within this plane, in terms of the amplitudes and relative phases of the first harmonics of the segments elevation angles. The model presented here sheds some new light on the possible interactions among the Central Pattern Generators possibly underlying the control of biped locomotion. The model precisely specifies how any two segments in the limb interact, and how a change in gait velocity affects the orientation of the intersegmental coordination plane mainly through a change in phase shifts between the segments. Implications of this study with respect to neural control of locomotion and other motor activities are discussed.