Jelena Novosel
Delft University of Technology
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Publication
Featured researches published by Jelena Novosel.
Medical Image Analysis | 2015
Jelena Novosel; Gijs Thepass; H Lemij; Johannes F. de Boer; Koenraad A. Vermeer; Lucas J. van Vliet
Optical coherence tomography (OCT) yields high-resolution, three-dimensional images of the retina. Reliable segmentation of the retinal layers is necessary for the extraction of clinically useful information. We present a novel segmentation method that operates on attenuation coefficients and incorporates anatomical knowledge about the retina. The attenuation coefficients are derived from in-vivo human retinal OCT data and represent an optical property of the tissue. Then, the layers in the retina are simultaneously segmented via a new flexible coupling approach that exploits the predefined order of the layers. The accuracy of the method was evaluated on 20 peripapillary scans of healthy subjects. Ten of those subjects were imaged again to evaluate the reproducibility. An additional evaluation was performed to examine the robustness of the method on a variety of data: scans of glaucoma patients, macular scans and scans by a two different OCT imaging devices. A very good agreement on all data was found between the manual segmentation performed by a medical doctor and the segmentation obtained by the automatic method. The mean absolute deviation for all interfaces in all data types varied between 1.9 and 8.5 µm (0.5-2.2 pixels). The reproducibility of the automatic method was similar to the reproducibility of the manual segmentation.
international symposium on biomedical imaging | 2013
Jelena Novosel; Koenraad A. Vermeer; Gijs Thepass; Hans G. Lemij; Lucas J. van Vliet
This paper presents a novel method for the segmentation of layered structures that have a predefined order. Layers are jointly segmented by simultaneous detection of their interfaces. This is done by means of a level set approach based on Bayesian inference where the ordering of the layers is enforced via a novel level set coupling. The method was applied to in-vivo images of healthy human retinas acquired by optical coherence tomography (OCT). A quantitative comparison with manual annotations was used to estimate the methods accuracy, which showed very good agreement (mean absolute deviation (MAD) of 3.11-8.58 μm). The large errors were mainly due to differences in handling the vessels. Based on repeated OCT images of the same eye acquired on consecutive days, the reproducibility of manual and automated segmentations, expressed by the MAD of the RNFL thickness, were 10.97 μm and 7.68 μm.
international symposium on biomedical imaging | 2016
Jelena Novosel; Ziyuan Wang; Henk de Jong; Mirjam E. J. van Velthoven; Koenraad A. Vermeer; Lucas J. van Vliet
We present a locally-adaptive approach to segment the fluid and the interfaces between retinal layers in eyes affected by central serous retinopathy based on loosely-coupled level sets. The approach exploits the local attenuation coefficient differences of layers around an interface and introduces auxiliary interfaces to delineate the fluid. Thus, it can handle abrupt attenuation coefficient variations and topology-disrupting anomalies. The method was applied to in-vivo images of retinas acquired by optical coherence tomography. A quantitative comparison with manual annotations shows the methods high accuracy: we obtained a mean absolute deviation for the interfaces of 3.7-8.9 ßm (1-2 pixels) and a Dice coefficient for the fluid segmentation of 0.96.
IEEE Transactions on Medical Imaging | 2017
Jelena Novosel; Koenraad A. Vermeer; Jan H. de Jong; Ziyuan Wang; Lucas J. van Vliet
Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9
Proceedings of SPIE | 2016
Jelena Novosel; Ziyuan Wang; Henk de Jong; Koenraad A. Vermeer; Lucas J. van Vliet
\mu \text{m}
international conference of the ieee engineering in medicine and biology society | 2015
Jelena Novosel; Koenraad A. Vermeer; Laurence Pierrache; Caroline C. W. Klaver; L. van den Born; Lucas J. van Vliet
(0.6–3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.
IEEE Transactions on Medical Imaging | 2017
Jelena Novosel; Suzanne Yzer; Koenraad A. Vermeer; Lucas J. van Vliet
Optical coherence tomography (OCT) is used to produce high-resolution three-dimensional images of the retina, which permit the investigation of retinal irregularities. In dry age-related macular degeneration (AMD), a chronic eye disease that causes central vision loss, disruptions such as drusen and changes in retinal layer thicknesses occur which could be used as biomarkers for disease monitoring and diagnosis. Due to the topology disrupting pathology, existing segmentation methods often fail. Here, we present a solution for the segmentation of retinal layers in dry AMD subjects by extending our previously presented loosely coupled level sets framework which operates on attenuation coefficients. In eyes affected by AMD, Bruch’s membrane becomes visible only below the drusen and our segmentation framework is adapted to delineate such a partially discernible interface. Furthermore, the initialization stage, which tentatively segments five interfaces, is modified to accommodate the appearance of drusen. This stage is based on Dijkstras algorithm and combines prior knowledge on the shape of the interface, gradient and attenuation coefficient in the newly proposed cost function. This prior knowledge is incorporated by varying the weights for horizontal, diagonal and vertical edges. Finally, quantitative evaluation of the accuracy shows a good agreement between manual and automated segmentation.
Investigative Ophthalmology & Visual Science | 2015
Maximilian Gräfe; L.S. Wilk; Boy Braaf; Jelena Novosel; Koenraad A. Vermeer; J. de Boer
This paper presents a method to determine the number of visible layers in the outer retina and perform segmentation. Each layer in the outer retina is represented by a Gaussian function, and multiple models with a different number of layers are used to form the outer retina. Parameters of competing models are calculated by using maximum likelihood estimation after which the model that best describes the data is selected. Model selection is based on the goodness of fit and model complexity thereby ensuring that the model that best represents the data is chosen. The method was applied to in-vivo macular images of human retinas acquired by optical coherence tomography after conversion to attenuation coefficients. Examples of detected number of visible layers and corresponding segmentation results are shown in both normal and retinitis pigmentosa affected retinas.
Investigative Ophthalmology & Visual Science | 2016
Jelena Novosel; Lucas J. van Vliet; Ziyuan Wang; Jan H. de Jong; Koenraad A. Vermeer
Extraction of image-based biomarkers, such as the presence, visibility, or thickness of a certain layer, from 3-D optical coherence tomography data provides relevant clinical information. We present a method to simultaneously determine the number of visible layers in the outer retina and segment them. The method is based on a model selection approach with special attention given to the balance between the quality of a fit and model complexity. This will ensure that a more complex model is selected only if this is sufficiently supported by the data. The performance of the method was evaluated on healthy and retinitis pigmentosa (RP) affected eyes. In addition, the reproducibility of automatic method and manual annotations was evaluated on healthy eyes. Good agreement between the segmentation performed manually by amedical doctor and results obtained fromthe automatic segmentation was found. The mean unsigned deviation for all outer retinal layers in healthy and RP affected eyes varied between 2.6 and
Investigative Ophthalmology & Visual Science | 2016
Gijs Thepass; Jelena Novosel; Hans G. Lemij; Koen A. Vermeer
4.9~\mu \text{m}