Motion-compensated optical coherence tomography using envelope-based surface detection and Kalman-based prediction

Irsch, Kristina; Lee, Mi Kyung; Bose, Sanjukta N.; Kang, Jin U.

doi:10.1117/12.2288545

Cited by 2 publications

(3 citation statements)

References 15 publications

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“…To further enhance FF-OCT technology and enable deeper penetration into the corneal tissue and beyond, we are also exploring axial motion compensation (collaboration with Prof. Jin Kang, at the Johns Hopkins Whiting School of Engineering [11]) and light manipulation (collaboration with Dr. Alexandre Aubry, at the Institut Langevin [12,13], an expert in light manipulation techniques, in particular the backscattering matrix approach [14,15]) techniques.…”

Section: Methods and Resultsmentioning

confidence: 99%

Towards In-Vivo Characterization and Deep Imaging of the Cornea and Beyond

Irsch¹

2018

Latin America Optics and Photonics Conference

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Section: Methods and Resultsmentioning

confidence: 99%

Towards In-Vivo Characterization and Deep Imaging of the Cornea and Beyond

Irsch¹

2018

Latin America Optics and Photonics Conference

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“…Motion estimation is a relevant problem for intraoperative OCT applications, for example in the context of motion compensation [13] and surgical tool navigation [28]. While previous approaches for motion estimation relied on a template and moving images, we learn a motion vector from an entire sequences of OCT volumes.…”

Section: Discussionmentioning

confidence: 99%

“…Alternatively, the OCT images can be used directly for motion estimation as OCT already offers a high spatial resolution. For example, Irsch et al estimated the tissue surface distance from A-scans for axial FOV adjustment [13]. 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].…”

Section: Introductionmentioning

confidence: 99%

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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Motion-compensated optical coherence tomography using envelope-based surface detection and Kalman-based prediction

Cited by 2 publications

References 15 publications

Towards In-Vivo Characterization and Deep Imaging of the Cornea and Beyond

Towards In-Vivo Characterization and Deep Imaging of the Cornea and Beyond

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

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