Repetitive motion compensation for real time intraoperative video processing

Abstract

In this paper, we present a motion compensation algorithm dedicated to video processing during neurosurgery. After craniotomy, the brain surface undergoes a repetitive motion due to the cardiac pulsation. This motion as well as potential video camera motion prevent accurate video analysis. We propose a dedicated motion model where the brain deformation is described using a linear basis learned from a few initial frames of the video. As opposed to other works using linear basis for the flow, the camera motion is explicitly accounted in the transformation model. Despite the nonlinear nature of our model, all the motion parameters are robustly estimated all at once, using only one singular value decomposition (SVD), making our procedure computationally efficient. A Lagrangian specification of the flow field ensures the stability of the method. Experiments on in vivo data are presented to evaluate the capacity of the method to cope with occlusion or camera motion. The method we propose satisfies the intraoperative constraints: it is robust to surgical tools occlusions, it works in real time, and it is able to handle large camera viewpoint changes.

Intraoperative Resting-State Functional Connectivity Based on RGB Imaging

“…For this purpose Monte Carlo simulations were performed using MCX software [26]. For each frame of the video, the repetitive brain motion was compensated [27,28]. The slow drift of RGB intensities was corrected due to tissue desiccation [29] and a low-pass filtering was performed to isolate slow hemodynamic fluctuations (cut-off frequency: 0.08 Hz [13,20,21]).…”

Intraoperative quantitative functional brain mapping using an RGB camera

“…The motion compensation aims to ensure that each camera pixel corresponds to the same cortical area all along data acquisition. Sdika et al 29,30 proposed a repetitive motion compensation algorithm. This algorithm is split into two parts.…”