Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT

Weiss J, Sommersperger M, Nasseri A, Eslami A, Eck U, Navab N (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12265 LNCS

Pages Range: 267-276

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Lima, PER

ISBN: 9783030597214

DOI: 10.1007/978-3-030-59722-1_26

Abstract

Intraoperative Optical Coherence Tomography (iOCT) has advanced in recent years to provide real-time high resolution volumetric imaging for ophthalmic surgery. It enables real-time 3D feedback during precise surgical maneuvers. Intraoperative 4D OCT generally exhibits lower signal-to-noise ratio compared to diagnostic OCT and visualization is complicated by instrument shadows occluding retinal tissue. Additional constraints of processing data rates upwards of 6 GB/s create unique challenges for advanced visualization of 4D OCT. Prior approaches for real-time 4D iOCT rendering have been limited to applying simple denoising filters and colorization to improve visualization. We present a novel real-time rendering pipeline that provides enhanced intraoperative visualization and is specifically designed for the high data rates of 4D iOCT. We decompose the volume into a static part consisting of the retinal tissue and a dynamic part including the instrument. Aligning the static parts over time allows temporal compounding of these structures for improved image quality. We employ a translational motion model and use axial projection images to reduce the dimensionality of the alignment. A model-based instrument segmentation on the projections discriminates static from dynamic parts and is used to exclude instruments from the compounding. Our real-time rendering method combines the compounded static information with the latest iOCT data to provide a visualization which compensates instrument shadows and improves instrument visibility. We evaluate the individual parts of our pipeline on pre-recorded OCT volumes and demonstrate the effectiveness of our method on a recorded volume sequence with a moving retinal forceps.

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Klinikum rechts der Isar DE Germany (DE) Carl Zeiss AG DE Germany (DE)

How to cite

APA:

Weiss, J., Sommersperger, M., Nasseri, A., Eslami, A., Eck, U., & Navab, N. (2020). Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT. In Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 267-276). Lima, PER: Springer Science and Business Media Deutschland GmbH.

MLA:

Weiss, Jakob, et al. "Processing-Aware Real-Time Rendering for Optimized Tissue Visualization in Intraoperative 4D OCT." Proceedings of the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, Lima, PER Ed. Anne L. Martel, Purang Abolmaesumi, Danail Stoyanov, Diana Mateus, Maria A. Zuluaga, S. Kevin Zhou, Daniel Racoceanu, Leo Joskowicz, Springer Science and Business Media Deutschland GmbH, 2020. 267-276.

BibTeX: Download