A Fast and Reliable Three-Dimensional Centerline Tracing: Application to Virtual Cochlear Implant Surgery

Abstract

This paper presents a rapid and unsupervised three-dimensional (3D) tubular structure tracing algorithm for the detection of safe trajectories in cochlear surgery. The algorithm utilizes a generalized 3D cylinder model which offers low-order parameterization, enabling low-cost recursive directional tubular boundary analysis and derivation of tubular statistics (i.e. centerline coordinates). Unlike previous work, the proposed algorithm circumvents excessive computation per voxel while enhancing angular centerline traversing efficiency which is critical in cochlear implant surgery navigation. To accomplish this, design considerations include: 1) accurate engineering of kernels used for border analysis, 2) modifying decisionmaking in identifying optimal tracing angle with homogeneity criterion, 3) reducing tubular change exploratory search cost through discrete convolution analysis, and 4) a cross-section calibration engine which suppresses centerline angular deviations as well as recording a history of geometrical changes while tracing. When evaluated on synthetic imagery mimicking cochlea structural complexity and real reconstructed cochlea models, it consistently produced accurate estimates of centerline coordinates and widths-heights in the presence of noise and spatial artefacts. Validation has shown that the centerline error for the proposed algorithm is below 6 pixels and the average traced pixel performance is 92.9% of the true centerline pixels on the examined cochlea models. By restricting the image analysis to the regions of interest, the proposed algorithm performs the rapid centerline tracing of the cochlea needed for real-time surgery (0.48 seconds per electrode insertion).

DeepNav: Joint View Learning for Direct Optimal Path Perception in Cochlear Surgical Platform Navigation

“…The sparse exploration of the boundaries yields low computational overhead but also introduces higher sensitivity to the discontinuities and geometrical complexities. An algorithm utilizing vectorization approach to handle 3D (volumetric) data is described in [31]. It is a fully automatic 3D neuron tracing algorithm emulating a 3D cylinder model and recursively explores the neuron topology.…”

High-Level Hessian-Based Image Processing with the Frangi Neuron

“…Among the most popular are deep neural networks [4], particularly U-network-based architectures [5], directional convolutional kernels [6], various approaches to region growing [7] and applied mathematical morphology [8]. In recent years, there have been several proposals to improve the response of hessian-based methods by, for example, using swarm optimization [9], genetic programming and other approaches [10][11][12][13][14][15][16][17].…”