Feasibility of a markerless tracking system based on optical coherence tomography

Schlüter, Matthias; Otte, Christoph; Saathoff, Thore; Gessert, Nils; Schlaefer, Alexander

doi:10.1117/12.2512178

Cited by 6 publications

(8 citation statements)

References 15 publications

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“…Also, Laves et al used conventional features such as SIFT [18] and SURF [1] with 2D maximum intensity projects for motion estimation in the context of volume of interests stabilization with OCT [17]. Another approach for high-speed OCT tracking relied on phase correlation for fast motion estimation from OCT images [20]. These approaches rely on hand-crafted features which can be error-prone, and the overall motion estimation accuracy is often limited [16].…”

Section: Introductionmentioning

confidence: 99%

“…So far, deep learning-based motion estimation with OCT relied on an initial template volume and a moving image, following the concept of registration-based motion estimation, for example, using phase correlation [20]. This can be problematic if motion between the original template and the current state is very large as the overlap between the images becomes small.…”

Section: Introductionmentioning

confidence: 99%

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Spatio-temporal deep learning methods for motion estimation using 4D OCT image data

et al. 2020

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Purpose Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. Optical coherence tomography (OCT) is an imaging modality with a high spatial and temporal resolution that has been used for intraoperative imaging and also for motion estimation, for example, in the context of ophthalmic surgery or cochleostomy. Recently, motion estimation between a template and a moving OCT image has been studied with deep learning methods to overcome the shortcomings of conventional, feature-based methods. Methods We investigate whether using a temporal stream of OCT image volumes can improve deep learning-based motion estimation performance. For this purpose, we design and evaluate several 3D and 4D deep learning methods and we propose a new deep learning approach. Also, we propose a temporal regularization strategy at the model output. Results Using a tissue dataset without additional markers, our deep learning methods using 4D data outperform previous approaches. The best performing 4D architecture achieves an correlation coefficient (aCC) of 98.58% compared to 85.0% of a previous 3D deep learning method. Also, our temporal regularization strategy at the output further improves 4D model performance to an aCC of 99.06%. In particular, our 4D method works well for larger motion and is robust toward image rotations and motion distortions. Conclusions We propose 4D spatio-temporal deep learning for OCT-based motion estimation. On a tissue dataset, we find that using 4D information for the model input improves performance while maintaining reasonable inference times. Our regularization strategy demonstrates that additional temporal information is also beneficial at the model output.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatio-temporal deep learning methods for motion estimation using 4D OCT image data

et al. 2020

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“…for prostate radiation therapy [3], or for precise pose estimation of a marker object [4]. Some modalities provide not only volumetric images, but also allow for imaging with a high temporal resolution, such as optical coherence tomography (OCT), and hence can be used as an imaging modality for an optical tracking system [5,6].…”

Section: Introductionmentioning

confidence: 99%

4D spatio-temporal convolutional networks for object position estimation in OCT volumes

Bengs

Gessert

Schlaefer

2020

Current Directions in Biomedical Engineering

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Tracking and localizing objects is a central problem in computer-assisted surgery. Optical coherence tomography (OCT) can be employed as an optical tracking system, due to its high spatial and temporal resolution. Recently, 3D convolutional neural networks (CNNs) have shown promising performance for pose estimation of a marker object using single volumetric OCT images. While this approach relied on spatial information only, OCT allows for a temporal stream of OCT image volumes capturing the motion of an object at high volumes rates. In this work, we systematically extend 3D CNNs to 4D spatio-temporal CNNs to evaluate the impact of additional temporal information for marker object tracking. Across various architectures, our results demonstrate that using a stream of OCT volumes and employing 4D spatio-temporal convolutions leads to a 30% lower mean absolute error compared to single volume processing with 3D CNNs.

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“…They usually involve moving the OCT scan head-for example, by integration into a motorized microscope [29] or by mounting it to a robot [30], and some kind of volume stitching [31]. As an alternative which does not require moving the scanning system, we recently proposed a setup which uses a second pair of galvos to laterally move the FOV to different locations [32]. By additionally using a motorized reference arm, the FOV can also be shifted axially.…”

Section: Introductionmentioning

confidence: 99%

Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography

Schlüter

Glandorf

Gromniak

et al. 2020

Sensors

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Optical tracking systems are widely used, for example, to navigate medical interventions. Typically, they require the presence of known geometrical structures, the placement of artificial markers, or a prominent texture on the target’s surface. In this work, we propose a 6D tracking approach employing volumetric optical coherence tomography (OCT) images. OCT has a micrometer-scale resolution and employs near-infrared light to penetrate few millimeters into, for example, tissue. Thereby, it provides sub-surface information which we use to track arbitrary targets, even with poorly structured surfaces, without requiring markers. Our proposed system can shift the OCT’s field-of-view in space and uses an adaptive correlation filter to estimate the motion at multiple locations on the target. This allows one to estimate the target’s position and orientation. We show that our approach is able to track translational motion with root-mean-squared errors below 0.25 mm and in-plane rotations with errors below 0.3°. For out-of-plane rotations, our prototypical system can achieve errors around 0.6°.

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Feasibility of a markerless tracking system based on optical coherence tomography

Cited by 6 publications

References 15 publications

Spatio-temporal deep learning methods for motion estimation using 4D OCT image data

Spatio-temporal deep learning methods for motion estimation using 4D OCT image data

4D spatio-temporal convolutional networks for object position estimation in OCT volumes

Concept for Markerless 6D Tracking Employing Volumetric Optical Coherence Tomography

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