Dataset Information

A pilot study of optical neuronavigation-guided brain biopsy in the horse using anatomic landmarks and fiducial arrays for patient registration.

ABSTRACT:

Background

Optical neuronavigation-guided intracranial surgery has become increasingly common in veterinary medicine, but its use has not yet been described in horses.

Objectives

To determine the feasibility of optical neuronavigation-guided intracranial biopsy procedures in the horse, compare the use of the standard fiducial array and anatomic landmarks for patient registration, and evaluate surgeon experience.

Animals

Six equine cadaver heads.

Methods

Computed tomography images of each specimen were acquired, with the fiducial array rigidly secured to the frontal bone. Six targets were selected in each specimen. Patient registration was performed separately for 3 targets using the fiducial array, and for 3 targets using anatomic landmarks. In lieu of biopsy, 1 mm diameter wire seeds were placed at each target. Postoperative images were coregistered with the planning scan to calculate Euclidian distance from the tip of the seed to the target.

Results

No statistical difference between registration techniques was identified. The impact of surgeon experience was examined for each technique using a Mann-Whitney U test. The experienced surgeon was significantly closer to the intended target (median = 2.52 mm) than were the novice surgeons (median = 6.55 mm) using the fiducial array (P = .001). Although not statistically significant (P = .31), for the experienced surgeon the median distance to target was similar when registering with the fiducial array (2.47 mm) and anatomic landmarks (2.58 mm).

Conclusions and clinical importance

Registration using both fiducial arrays and anatomic landmarks for brain biopsy using optical neuronavigation in horses is feasible.

SUBMITTER: Santistevan L

PROVIDER: S-EPMC7379038 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Publications

A pilot study of optical neuronavigation-guided brain biopsy in the horse using anatomic landmarks and fiducial arrays for patient registration.

Santistevan Lawrence L Easley Jeremiah J Ruple Audrey A Monck Sam S Randall Elissa E Wininger Fred F Packer Rebecca A RA

Journal of veterinary internal medicine 20200529 4

<h4>Background</h4>Optical neuronavigation-guided intracranial surgery has become increasingly common in veterinary medicine, but its use has not yet been described in horses.<h4>Objectives</h4>To determine the feasibility of optical neuronavigation-guided intracranial biopsy procedures in the horse, compare the use of the standard fiducial array and anatomic landmarks for patient registration, and evaluate surgeon experience.<h4>Animals</h4>Six equine cadaver heads.<h4>Methods</h4>Computed tomo ...[more]

PMID: 32469442

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Project description:ObjectiveDetecting and measuring changes in longitudinal fundus imaging is key to monitoring disease progression in chronic ophthalmic diseases, such as glaucoma and macular degeneration. Clinicians assess changes in disease status by either independently reviewing or manually juxtaposing longitudinally acquired color fundus photos (CFPs). Distinguishing variations in image acquisition due to camera orientation, zoom, and exposure from true disease-related changes can be challenging. This makes manual image evaluation variable and subjective, potentially impacting clinical decision-making. We introduce our deep learning (DL) pipeline, "EyeLiner," for registering, or aligning, 2-dimensional CFPs. Improved alignment of longitudinal image pairs may compensate for differences that are due to camera orientation while preserving pathological changes.DesignEyeLiner registers a "moving" image to a "fixed" image using a DL-based keypoint matching algorithm.ParticipantsWe evaluate EyeLiner on 3 longitudinal data sets: Fundus Image REgistration (FIRE), sequential images for glaucoma forecast (SIGF), and our internal glaucoma data set from the Colorado Ophthalmology Research Information System (CORIS).MethodsAnatomical keypoints along the retinal blood vessels were detected from the moving and fixed images using a convolutional neural network and subsequently matched using a transformer-based algorithm. Finally, transformation parameters were learned using the corresponding keypoints.Main outcome measuresWe computed the mean distance (MD) between manually annotated keypoints from the fixed and the registered moving image. For comparison to existing state-of-the-art retinal registration approaches, we used the mean area under the curve (AUC) metric introduced in the FIRE data set study.ResultsEyeLiner effectively aligns longitudinal image pairs from FIRE, SIGF, and CORIS, as qualitatively evaluated through registration checkerboards and flicker animations. Quantitative results show that the MD decreased for this model after alignment from 321.32 to 3.74 pixels for FIRE, 9.86 to 2.03 pixels for CORIS, and 25.23 to 5.94 pixels for SIGF. We also obtained an AUC of 0.85, 0.94, and 0.84 on FIRE, CORIS, and SIGF, respectively, beating the current state-of-the-art SuperRetina (AUCFIRE = 0.76, AUCCORIS = 0.83, AUCSIGF = 0.74).ConclusionsOur pipeline demonstrates improved alignment of image pairs in comparison to the current state-of-the-art methods on 3 separate data sets. We envision that this method will enable clinicians to align image pairs and better visualize changes in disease over time.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

A pilot study of optical neuronavigation-guided brain biopsy in the horse using anatomic landmarks and fiducial arrays for patient registration.

Background

Objectives

Animals

Methods

Results

Conclusions and clinical importance

Publications

A pilot study of optical neuronavigation-guided brain biopsy in the horse using anatomic landmarks and fiducial arrays for patient registration.

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