Tracking of Vessels in Intra-Operative Microscope Video Sequences for Cortical Displacement Estimation

Abstract

This article presents a method designed to automatically track cortical vessels in intra-operative microscope video sequences. The main application of this method is the estimation of cortical displacement that occurs during tumor resection procedures. The method works in three steps. First, models of vessels selected in the first frame of the sequence are built. These models are then used to track vessels across frames in the video sequence. Finally, displacements estimated using the vessels are extrapolated to the entire image. The method has been tested retrospectively on images simulating large displacement, tumor resection, and partial occlusion by surgical instruments and on 21 video sequences comprising several thousand frames acquired from three patients. Qualitative results show that the method is accurate, robust to the appearance and disappearance of surgical instruments, and capable of dealing with large differences in images caused by resection. Quantitative results show a mean vessel tracking error (VTE) of 2.4 pixels (0.3 or 0.6 mm, depending on the spatial resolution of the images) and an average target registration error (TRE) of 3.3 pixels (0.4 or 0.8 mm).

Patient Registration Using Intraoperative Stereovision in Image-guided Open Spinal Surgery

“…Radiation-free intraoperative images from ultrasound (US; [17]–[22]), stereovision (iSV; [23]–[25]), laser range scanning (LRS) [26], [27], and more recently, conoscopic holography [28] have been employed to aid navigation in image-guided surgical interventions. Both feature- and intensity-based methods can be used to register US with CT of the spine.…”

“…They have been successfully employed to register either with preoperative magnetic resonance images of the brain [26], [27], [32], [33] or with each other, when acquired at two temporally distinct surgical stages [24], [27], [34], [35], to compensate for intraoperative brain shift. Because reconstructed stereoscopic surfaces provide both 3D geometry and texture intensity, either source of information can be employed for registration separately or in combination.…”

“…On the other hand, when registering two textured 3D surfaces acquired with iSV or LRS at two different points in time, the 3D surfaces can be projected into a common 2D coordinate system to transform the 3D geometrical surface registration problem into a simpler 2D image registration [34], [35]. This concept is especially appealing in clinical applications where the registration process needs to be fully automated, and can be achieved with optical-flow-based registration of the iSV images [35] without requiring manual feature segmentation (e.g., vessels [27]).…”

Cortical surface shift estimation using stereovision and optical flow motion tracking via projection image registration

“…Stereovision (Sun et al, 2005; Paul et al, 2005, 2009; DeLorenzo et al, 2010) and laser range scanning (Miga et al, 2003; Ding et al, 2011) capture 3D geometry and texture intensity of the exposed cortical surface, and have become important tools for brain shift compensation. With intraoperative stereovision (iSV), reconstructed cortical surfaces are registered either with pMR (Sun et al, 2005; DeLorenzo et al, 2006), or with each other when acquired at two temporally distinct surgical stages (Paul et al, 2009).…”

“…However, because positioning of the LRS device and acquisition of intraoperative data required on the order of 1 min, only temporally sparse measurements would most likely be available in a given case, and computing intermediate updates could become challenging in some cases (Kumar et al, 2013). More recently, Ding et al (2011) used intensity-encoded point clouds to reconstruct 3D surfaces where the corresponding 2D images were registered with video frames acquired through the surgical microscope to track segmented vessels in order to determine 3D displacements. Results from three patient cases suggested high accuracy in vessel tracking and registration (~3 pixels).…”