Deep-learning segmentation of fascicles from microCT of the human vagus nerve

IntroductionMicroCT of the three-dimensional fascicular organization of the human vagus nerve provides essential data to inform basic anatomy as well as the development and optimization of neuromodulation therapies. To process the images into usable formats for subsequent analysis and computational modeling, the fascicles must be segmented. Prior segmentations were completed manually due to the complex nature of the images, including variable contrast between tissue types and staining artifacts.MethodsHere, we developed a U-Net convolutional neural network (CNN) to automate segmentation of fascicles in microCT of human vagus nerve.ResultsThe U-Net segmentation of ~500 images spanning one cervical vagus nerve was completed in 24 s, versus ~40 h for manual segmentation, i.e., nearly four orders of magnitude faster. The automated segmentations had a Dice coefficient of 0.87, a measure of pixel-wise accuracy, thus suggesting a rapid and accurate segmentation. While Dice coefficients are a commonly used metric to assess segmentation performance, we also adapted a metric to assess fascicle-wise detection accuracy, which showed that our network accurately detects the majority of fascicles, but may under-detect smaller fascicles.DiscussionThis network and the associated performance metrics set a benchmark, using a standard U-Net CNN, for the application of deep-learning algorithms to segment fascicles from microCT images. The process may be further optimized by refining tissue staining methods, modifying network architecture, and expanding the ground-truth training data. The resulting three-dimensional segmentations of the human vagus nerve will provide unprecedented accuracy to define nerve morphology in computational models for the analysis and design of neuromodulation therapies

Spatiotemporal parameters for energy efficient kilohertz-frequency nerve block with low onset response

Abstract Background Electrical nerve conduction block has great potential for treatment of disease through reversible and local inactivation of somatic and autonomic nerves. However, the relatively high energy requirements and the presence of undesired excitation at the onset of the kilohertz-frequency (KHF) signals used for block pose obstacles to effective translation. Frequency, electrode geometry, and waveform shape are known to influence block threshold and onset response, but available data provide a limited understanding of how to select these parameters to optimize nerve block. Methods We evaluated KHF nerve block in rat tibial nerve across frequencies (5–60 kHz), electrode geometries (monopolar, bipolar, and tripolar), and waveform shapes. We present a novel Fourier-based method for constructing composite signals that systematically sample the KHF waveform design space. Results The lowest frequencies capable of blocking (5–16 kHz) were not the most energy-efficient among the tested frequencies. Further, bipolar cuffs required the largest current and power to block, monopolar cuffs required the lowest current, and both tripolar and monopolar cuffs required the lowest power. Tripolar cuffs produced the smallest onset response across frequencies. Composite signals comprised of a first harmonic sinusoid at fundamental frequency (f0) superposed on a second harmonic sinusoid at 2f0 could block at lower threshold and lower onset response compared to the constituent sinusoids alone. This effect was strongly dependent on the phase of the second harmonic and on the relative amplitudes of the first and second harmonics. This effect was also dependent on electrode geometry: monopolar and tripolar cuffs showed clear composite signal effects in most experiments; bipolar cuffs showed no clear effects in most experiments. Conclusions Our data provide novel information about block threshold and onset response at the boundary of frequencies that can block. Our results also show an interaction between spatial (cuff geometry) and temporal (frequency and waveform shape) parameters. Finally, while previous studies suggested that temporal parameters could reduce onset response only in exchange for increased block threshold (or vice versa), our results show that waveform shape influences KHF response in ways that can be exploited to reduce both energy and onset responses

Effect of Fiber Location Within Nerve.

BackgroundPeripheral nerve recordings can enhance the efficacy of neurostimulation therapies by providing a feedback signal to adjust stimulation settings for greater efficacy or reduced side effects. Computational models can accelerate the development of interfaces with high signal-to-noise ratio and selective recording. However, validation and tuning of model outputs against in vivo recordings remains computationally prohibitive due to the large number of fibers in a nerve.MethodsWe designed and implemented highly efficient modeling methods for simulating electrically evoked compound nerve action potential (CNAP) signals. The method simulated a subset of fiber diameters present in the nerve using NEURON, interpolated action potential templates across fiber diameters, and filtered the templates with a weighting function derived from fiber-specific conduction velocity and electromagnetic reciprocity outputs of a volume conductor model. We applied the methods to simulate CNAPs from rat cervical vagus nerve.ResultsBrute force simulation of a rat vagal CNAP with all 1,759 myelinated and 13,283 unmyelinated fibers in NEURON required 286 and 15,860 CPU hours, respectively, while filtering interpolated templates required 30 and 38 seconds on a desktop computer while maintaining accuracy. Modeled CNAP amplitude could vary by over two orders of magnitude depending on tissue conductivities and cuff opening within experimentally relevant ranges. Conduction distance and fiber diameter distribution also strongly influenced the modeled CNAP amplitude, shape, and latency. Modeled and in vivo signals had comparable shape, amplitude, and latency for myelinated fibers but not for unmyelinated fibers.ConclusionsHighly efficient methods of modeling neural recordings quantified the large impact that tissue properties, conduction distance, and nerve fiber parameters have on CNAPs. These methods expand the computational accessibility of neural recording models, enable efficient model tuning for validation, and facilitate the design of novel recording interfaces for neurostimulation feedback and understanding physiological systems.</div

Temporal Template Interpolation Revealed Necessary Precision for Accurate CNAP Reconstruction.

Temporal Template Interpolation Revealed Necessary Precision for Accurate CNAP Reconstruction.</p

CNAPs modeled with different numbers of templates by using linear interpolation across fiber diameters (1.013 to 9.809 μm for myelinated fibers and 0.105 to 1.896 μm for unmyelinated fibers) at five conduction distances.

(A-F) Example myelinated and unmyelinated CNAPs at conduction distances of 6, 21, and 81 mm. (G-H) Maximum percent discrepancy between CNAPn (i.e., CNAP constructed from n templates) and CNAPfinest (i.e., finest = 193 for myelinated or finest = 97 for unmyelinated): 100*max(abs(CNAPn—CNAPfinest))/Vpk-pk,finest.</p

In vivo CNAP recording setup overview.

(A) Surgical setup of stimulation and recording electrodes along the rat cervical vagus nerve. The black tick marks on the blue ruler are 1 mm apart. (B) Block diagram of stimulation (green) and recording (light blue) hardware setup. “G” denotes a unit plugged into wall power. “FHC bp isolator” is a current source, “Fluke” is a battery-powered oscilloscope, and “SR560” is a preamplifier. The “ground needle” in panel (A) was connected to the Faraday cage, while the “reference needle” was connected to channel B of all three SR560 units.</p

Effect of bin size and sampling method on myelinated fiber CNAPs during extraction of fiber diameters from distributions.

(A) Histograms of known myelinated fiber diameters across different bin sizes. A bin size of 0 μm used the individual fiber diameter measurements (precision of 1e-6 μm). (B) Effect on CNAPs of generating fiber diameters based on the center of the bin and the bin height. As bin size increased, using the bin centers produced inaccuracies due to less destructive interference and more constructive interference. (C) Effect on CNAPs of generating fiber diameters based on inverse transform sampling to sample diameters randomly from the estimated cumulative distribution function. For a given non-zero bin size, CNAPs were more accurate than when using bin centers.</p

Minimum Number of Templates Depended Strongly on Conduction Distance and Interpolation.

Minimum Number of Templates Depended Strongly on Conduction Distance and Interpolation.</p