TL;DR
HyKey introduces a hyperspectral imaging-based neural network for improved keypoint detection and matching in minimally invasive surgery, outperforming RGB methods in challenging surgical scenes.
Contribution
The paper presents HyKey, a novel hyperspectral keypoint detection model that leverages spectral-spatial features for better surgical scene understanding.
Findings
HyKey achieves 96.62% mean matching accuracy.
HyKey outperforms RGB-based methods on pose estimation.
Spectral information enhances robustness in texture-poor environments.
Abstract
Purpose: 3D reconstruction in minimally invasive surgery (MIS) enables enhanced surgical guidance through improved visualisation, tool tracking, and augmented reality. However, traditional RGB-based keypoint detection and matching pipelines struggle with surgical challenges, such as poor texture and complex illumination. We investigate whether using snapshot hyperspectral imaging (HSI) can provide improved results on keypoint detection and matching surgical scenes. Methods: We developed HyKey, a HYperspectral KEYpoint detection and description model made up of a hybrid 3D-2D convolutional neural network that jointly extracts spatial-spectral features from HSI. The model was trained using synthetic homographic augmentation and epipolar geometry constraints on a robotically-acquired dual-camera RGB-HSI laparoscopic dataset of ex-vivo organs with calibrated camera poses. We benchmarked…
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