Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning

Qin, Xi; Chen, Yu; Wang, Bohan; Boegner, David; Gaitan, Brandon; Zheng, Yingning; Du, Xian

doi:10.1109/tbme.2021.3116514

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…Alternative tissue analysis can be conducted using, for instance, deep-learning based reasoning for tumor diagnosis procedure 50 . It is also worth noting that the robotic arm provides superior dexterity and accuracy in positioning the OCT probe compared to hand-held 32 and translational stage solutions 34 . In the presented experiments, we mainly employed pure translational scan motion to ensure consistent optical attenuation across the tissue for ATCM generation.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, the system is not best suited for resolution-sensitive applications such as kidney imaging. Qin et al presented a hand-held OCT probe 32 which offers unconstrained FOV. The probe is tracked in six degree-of-freedom (DoF) using visual-odometry (VO) techniques 33 through an RGB-depth camera mounted on the probe which perceives the probe’s surrounding environment.…”

Section: Introductionmentioning

confidence: 99%

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Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Zhang,

Ma,

Moradi

et al. 2023

Preprint

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Optical coherence tomography (OCT) is a high-resolution imaging modality that can be used to image microstructures of human kidneys. These images can be analyzed to evaluate the viability of the organ for transplantation. However, current OCT devices suffer from insufficient field-of-view, leading to biased examination outcomes when only small portions of the kidney can be assessed. Here we present a robotic OCT system where an OCT probe is integrated with a robotic manipulator, enabling wider area spatially-resolved imaging. With the proposed system, it becomes possible to comprehensively scan the kidney surface and provide large area parameterization of the microstructures. We verified the probe tracking accuracy with a phantom as 0.0762 ± 0.0727 mm and demonstrated its clinical feasibility by scanning ex vivo kidneys. The parametric map exhibits fine vasculatures beneath the kidney surface. Quantitative analysis on the proximal convoluted tubule from the ex vivo human kidney yields highly clinical-relevant information.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Zhang,

Ma,

Moradi

et al. 2023

Preprint

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“…OCT has been shown to provide comprehensive information for disease detection and quantification, but it is not widely available in underprivileged areas. Synthesising OCT data from a more feasible modality, such as fundus photography, 68 or low‐cost OCT, 69 may improve disease detection and characterisation in lower‐resource areas. As OCT is a relatively new technology, medical students and ophthalmology residents require more training to acquire sufficient knowledge to interpret OCT scans, and such training may be limited in certain areas.…”

Section: Current Gaps For DL In Octmentioning

confidence: 99%

Deep learning in optical coherence tomography: Where are the gaps?

Ran

Cheung

et al. 2023

Clinical Exper Ophthalmology

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Optical coherence tomography (OCT) is a non‐invasive optical imaging modality, which provides rapid, high‐resolution and cross‐sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post‐processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL‐based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL‐based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real‐world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.

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“…The difference in the location of two corresponding pixels in two images is called disparity. The depth of a point is estimated by using the parameters of cameras, disparity and mathematical and geometric relations [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Framework for Generating Disparity Map from Stereo Images using Deep Learning

Karimi

Dolati

Layeghi

2022

Preprint

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One of the most important functions of the human vision system is the three-dimensional perception of the surrounding environment. The image formed on the retina is actually a reflection of light patterns emitted from the real environment. The two images formed in two eyes differ from each other due to the spatial difference of the two eyes in the horizontal direction, and every point in the three-dimensional space does not appear in the same position in the image of the two retinas, which is the basis of depth perception. These results became the source of extensive research to extract depth by means of two or more images taken from the same scene and from different positions. Creating the appearance of 3D shapes based on photographs by computational techniques is of great interest in the field of machine vision research. Challenges in existing methods such as high error rate and computational costs in these fields have led to new methods in this field. Today, we can create 3D models with images of physical environments and through machine vision methods. Today, machine vision has many applications, including optical character recognition, medical imaging, surveillance, security, and many other things. In this process, the pair of stereo images taken from the real world is used by calculating the disparity of the pixels to estimate the three-dimensional space. In this paper, a framework for creating a disparity map using deep learning is presented, which is presented to solve or reduce the existing problems. Our proposed method is called SIUDL, which is presented using deep learning and based on various criteria such as RMSE errors and 3-pixel error, it has been able to have a lower error rate and better efficiency than methods such as DispNet, GA-Net and PSMNet. The simulation results show that our proposed method has reduced the error rate by 60% compared to the stated methods.

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Indoor Localization of Hand-Held OCT Probe Using Visual Odometry and Real-Time Segmentation Using Deep Learning

Cited by 12 publications

References 27 publications

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Large Area Kidney Imaging for Pre-transplant Evaluation using Real-Time Robotic Optical Coherence Tomography

Deep learning in optical coherence tomography: Where are the gaps?

A Framework for Generating Disparity Map from Stereo Images using Deep Learning

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