The Experts below are selected from a list of 1052367 Experts worldwide ranked by ideXlab platform
Schlaefer Alexander - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data
2020Co-Authors: Bengs Marcel, Gessert Nils, Schlüter Matthias, Schlaefer AlexanderAbstract: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 towards 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.Comment: Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery (IJCARS
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Spatio-temporal deep Learning Methods for motion estimation using 4D OCT image data
Springer, 2020Co-Authors: Bengs Marcel, Schlüter Matthias, Gessert, Nils Thorben, Schlaefer AlexanderAbstract: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.This work was partially funded by Forschungszentrum Medizintechnik Hamburg (grants 04fmthh16)
Michael J. Collins - One of the best experts on this subject based on the ideXlab platform.
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Automatic choroidal segmentation in OCT images using supervised deep Learning Methods
Scientific Reports, 2019Co-Authors: Jason Kugelman, David Alonso-caneiro, Scott A. Read, Jared Hamwood, Stephen J. Vincent, Fred K. Chen, Michael J. CollinsAbstract:The analysis of the choroid in the eye is crucial for our understanding of a range of ocular diseases and physiological processes. Optical coherence tomography (OCT) imaging provides the ability to capture highly detailed cross-sectional images of the choroid yet only a very limited number of commercial OCT instruments provide Methods for automatic segmentation of choroidal tissue. Manual annotation of the choroidal boundaries is often performed but this is impractical due to the lengthy time taken to analyse large volumes of images. Therefore, there is a pressing need for reliable and accurate Methods to automatically segment choroidal tissue boundaries in OCT images. In this work, a variety of patch-based and fully-convolutional deep Learning Methods are proposed to accurately determine the location of the choroidal boundaries of interest. The effect of network architecture, patch-size and contrast enhancement Methods was tested to better understand the optimal architecture and approach to maximize performance. The results are compared with manual boundary segmentation used as a ground-truth, as well as with a standard image analysis technique. Results of total retinal layer segmentation are also presented for comparison purposes. The findings presented here demonstrate the benefit of deep Learning Methods for segmentation of the chorio-retinal boundary analysis in OCT images.
Somayeh Delavari - One of the best experts on this subject based on the ideXlab platform.
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using kirkpatrick s model to measure the effect of a new teaching and Learning Methods workshop for health care staff
BMC Research Notes, 2019Co-Authors: Mohammad Reza Heydari, Fatemeh Taghva, Mitra Amini, Somayeh DelavariAbstract:This study is designed to evaluate the effect of a workshop about new teaching and Learning Methods on the response, knowledge, and behavior of healthcare staff working a large city healthcare center. Kirkpatrick’s program evaluation model showed that the workshop on new teaching and Learning Methods significantly improved the healthcare staff’s satisfaction about the teaching environment of workshops, their knowledge about new teaching and Learning Methods and their behavior in performing workshops for teaching people. It is recommended that this teaching and Learning Methods workshop should be considered in educational programs for healthcare staff. Trial registration Trial registration number: IRCT20180619040150N1 approved by Iranian Registry of Clinical Trials at 2018-07-27
Ryosuke Shibasaki - One of the best experts on this subject based on the ideXlab platform.
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identification of village building via google earth images and supervised machine Learning Methods
Remote Sensing, 2016Co-Authors: Zhiling Guo, Wataru Ohira, Hiroyuki Miyazaki, Yongwei Xu, Xiaowei Shao, Ryosuke ShibasakiAbstract:In this study, a method based on supervised machine Learning is proposed to identify village buildings from open high-resolution remote sensing images. We select Google Earth (GE) RGB images to perform the classification in order to examine its suitability for village mapping, and investigate the feasibility of using machine Learning Methods to provide automatic classification in such fields. By analyzing the characteristics of GE images, we design different features on the basis of two kinds of supervised machine Learning Methods for classification: adaptive boosting (AdaBoost) and convolutional neural networks (CNN). To recognize village buildings via their color and texture information, the RGB color features and a large number of Haar-like features in a local window are utilized in the AdaBoost method; with multilayer trained networks based on gradient descent algorithms and back propagation, CNN perform the identification by mining deeper information from buildings and their neighborhood. Experimental results from the testing area at Savannakhet province in Laos show that our proposed AdaBoost method achieves an overall accuracy of 96.22% and the CNN method is also competitive with an overall accuracy of 96.30%.
Bengs Marcel - One of the best experts on this subject based on the ideXlab platform.
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Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data
2020Co-Authors: Bengs Marcel, Gessert Nils, Schlüter Matthias, Schlaefer AlexanderAbstract: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 towards 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.Comment: Accepted for publication in the International Journal of Computer Assisted Radiology and Surgery (IJCARS
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Spatio-temporal deep Learning Methods for motion estimation using 4D OCT image data
Springer, 2020Co-Authors: Bengs Marcel, Schlüter Matthias, Gessert, Nils Thorben, Schlaefer AlexanderAbstract: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.This work was partially funded by Forschungszentrum Medizintechnik Hamburg (grants 04fmthh16)
