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

Abstract

Although much research has been conducted in the field of automated cochlear implant navigation, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as identifying the optimal navigation zone (OPZ) in the cochlear. In this paper, a 2.5D joint-view convolutional neural network (2.5D CNN) is proposed and evaluated for the identification of the OPZ in the cochlear segments. The proposed network consists of two complementary sagittal and bird-view (or top view) networks for the 3D OPZ recognition, each utilizing a ResNet-8 architecture consisting of five convolutional layers with rectified nonlinearity unit (ReLU) activations, followed by average pooling with a size equal to the size of the final feature maps. The last fully connected layer of each network has four indicators, equivalent to the classes considered: the distance to the adjacent left and right walls, collision probability and heading angle. To demonstrate this, the 2.5D CNN was trained using a parametric data generation model, and then evaluated using anatomically constructed cochlea models from micro-CT images of different cases. Prediction of the indicators demonstrates the effectiveness of the 2.5D CNN, for example, the heading angle has less than 1° error with computation delays of less that <1 milliseconds.

Keywords

• Automated insertion
• virtual surgery
• cochlear implant
• convolutional neural network
• real-time systems
• low-cost navigation