Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors

Rüegsegger, Michael B.; Cuadra, Meritxell Bach; Pica, Alessia; Amstutz, Christoph; Rudolph, Tobias; Aebersold, Daniel M.; Kowal, Jens

doi:10.1016/j.ijrobp.2012.05.040

Cited by 17 publications

(18 citation statements)

References 14 publications

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“…Note that this model is trained exclusively on pathological eye MRI data, in contrast to our previous work [15, 16]. This model aims at delineating the regions of the sclera, the cornea, the lens and the vitreous humor in an automatic fashion.…”

Section: Methodsmentioning

confidence: 99%

“…Also, other approaches such as the one presented by Beenakker et al [13] aimed at measuring the three-dimensional shape of the retina to study abnormal shape changes and peripheral vision. Along this lines, Active Shape Models (ASMs), originally introduced by Cootes et al [14] have recently been proposed by our group to more precisely delineate eye anatomy using T1w Volumetric Interpolated Brain Examination (VIBE) MR and CT images [15, 16]. In that work, the main goal was to delineate healthy eye structures, but the challenge of eye tumor delineation has yet to be addressed.…”

Section: Introductionmentioning

confidence: 99%

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Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

et al. 2017

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Retinoblastoma and uveal melanoma are fast spreading eye tumors usually diagnosed by using 2D Fundus Image Photography (Fundus) and 2D Ultrasound (US). Diagnosis and treatment planning of such diseases often require additional complementary imaging to confirm the tumor extend via 3D Magnetic Resonance Imaging (MRI). In this context, having automatic segmentations to estimate the size and the distribution of the pathological tissue would be advantageous towards tumor characterization. Until now, the alternative has been the manual delineation of eye structures, a rather time consuming and error-prone task, to be conducted in multiple MRI sequences simultaneously. This situation, and the lack of tools for accurate eye MRI analysis, reduces the interest in MRI beyond the qualitative evaluation of the optic nerve invasion and the confirmation of recurrent malignancies below calcified tumors. In this manuscript, we propose a new framework for the automatic segmentation of eye structures and ocular tumors in multi-sequence MRI. Our key contribution is the introduction of a pathological eye model from which Eye Patient-Specific Features (EPSF) can be computed. These features combine intensity and shape information of pathological tissue while embedded in healthy structures of the eye. We assess our work on a dataset of pathological patient eyes by computing the Dice Similarity Coefficient (DSC) of the sclera, the cornea, the vitreous humor, the lens and the tumor. In addition, we quantitatively show the superior performance of our pathological eye model as compared to the segmentation obtained by using a healthy model (over 4% DSC) and demonstrate the relevance of our EPSF, which improve the final segmentation regardless of the classifier employed.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

et al. 2017

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“…Since CT and OCT has been acquired at the same time, topography and ocular CT can be registered using fiducial markers. Finally, treatment planning can be performed based on an extended version of the statistical eye model, 25 which is adapted to the patient's anatomy based on the automatic segmentation and registration of structures of interest in ocular CT and MRT data. Application of the tracking device during MRT scanning is not required.…”

Section: D Treatment Planningmentioning

confidence: 99%

“…25 In the meantime we extended the model to be also applied to ocular MRI imaging in order to include 3D tumor shape in the treatment plan. This work introduces the first steps towards a CT compatible, head-mounted device for noninvasive referencing of intraocular tumors to enhance quality and efficacy of EBRT.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive referencing of intraocular tumors for external beam radiation therapy using optical coherence tomography: A proof of concept

et al. 2014

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“…Quantifying and dealing with such uncertainties has been explored in biomedical modeling and computational simulation to support diagnosis (González Ballester et al, 2000, 2002, 2004; Kohara et al, 2011; Schmid et al, 2011), pre and post-operative decisions (Talib et al, 2005; Rajamani et al, 2007; Bode et al, 2012), and therapy treatments (Belenguer et al, 2006; Zheng et al, 2007, 2009; Bednarz et al, 2011; Cao et al, 2012; Rüegsegger et al, 2012). Uncertainties influence in both the computation of the output and its interpretation.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

Mangado

Piella

Noailly

et al. 2016

Front. Bioeng. Biotechnol.

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Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering.

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Statistical Modeling of the Eye for Multimodal Treatment Planning for External Beam Radiation Therapy of Intraocular Tumors

Cited by 17 publications

References 14 publications

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

Multi-channel MRI segmentation of eye structures and tumors using patient-specific features

Noninvasive referencing of intraocular tumors for external beam radiation therapy using optical coherence tomography: A proof of concept

Analysis of Uncertainty and Variability in Finite Element Computational Models for Biomedical Engineering: Characterization and Propagation

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