Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model

Zanet, Sandro I. De; Ciller, Carlos; Rudolph, Tobias; Maeder, Philippe; Munier, Francis L.; Balmer, Aubin; Cuadra, Meritxell Bach; Kowal, Jens

doi:10.1109/tbme.2014.2359676

Cited by 11 publications

(9 citation statements)

References 28 publications

(35 reference statements)

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“…Translational motion of the head during dynamic MR data acquisition was estimated using an efficient subpixel image registration by cross-correlation algorithm ( Guizar-Sicairos et al, 2008 ). For both static 3D and dynamic 2D MR data, an initial estimation of eyeball center was obtained using the fast radial symmetry transform ( Loy and Zelinsky, 2003 ; De Zanet et al, 2015 ) under the assumption that a typical eyeball is ∼24 mm in diameter. The approximate eyeball center positions were used to crop the MR data and as a starting point for later analysis.…”

Section: Methodsmentioning

confidence: 99%

Real-Time MRI Reveals Unique Insight into the Full Kinematics of Eye Movements

Kirchner

Watson

Lappe

2021

eNeuro

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Section: Methodsmentioning

confidence: 99%

Real-Time MRI Reveals Unique Insight into the Full Kinematics of Eye Movements

Kirchner

Watson

Lappe

2021

eNeuro

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“…In‐house algorithms have been developed to semi‐automatically segment eye anatomies and uveal melanomas on no contrast‐enhanced T1‐ and T2‐weighted images 29 . The center of the eye was estimated on T2‐weighted images using the fast‐radial symmetry transform 30 . This center was used to build a 3D‐triangulated‐surface mesh to detect the inner and outer borders of the sclera and the tumor boundary itself.…”

Section: Methodsmentioning

confidence: 99%

“…29 The center of the eye was estimated on T2-weighted images using the fast-radial symmetry transform. 30 This center was used to build a 3D-triangulated-surface mesh to detect the inner and outer borders of the sclera and the tumor boundary itself. The inner scleral mask (including the cornea) was created to register T1-and T2-weighted images using Elastix, 31,32 with normalized mutual information as a similarity metric.…”

Section: C2 Semi-automatic Mri-segmentationmentioning

confidence: 99%

Three‐dimensional MRI‐based treatment planning approach for non‐invasive ocular proton therapy

et al. 2021

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Purpose To develop a high‐resolution three‐dimensional (3D) magnetic resonance imaging (MRI)‐based treatment planning approach for uveal melanomas (UM) in proton therapy. Materials/methods For eight patients with UM, a segmentation of the gross tumor volume (GTV) and organs‐at‐risk (OARs) was performed on T1‐ and T2‐weighted 7 Tesla MRI image data to reconstruct the patient MR‐eye. An extended contour was defined with a 2.5‐mm isotropic margin derived from the GTV. A broad beam algorithm, which we have called πDose, was implemented to calculate relative proton absorbed doses to the ipsilateral OARs. Clinically favorable gazing angles of the treated eye were assessed by calculating a global weighted‐sum objective function, which set penalties for OARs and extreme gazing angles. An optimizer, which we have named OPT’im‐Eye‐Tool, was developed to tune the parameters of the functions for sparing critical‐OARs. Results In total, 441 gazing angles were simulated for every patient. Target coverage including margins was achieved in all the cases (V95% > 95%). Over the whole gazing angles solutions space, maximum dose (Dmax) to the optic nerve and the macula, and mean doses (Dmean) to the lens, the ciliary body and the sclera were calculated. A forward optimization was applied by OPT’im‐Eye‐Tool in three different prioritizations: iso‐weighted, optic nerve prioritized, and macula prioritized. In each, the function values were depicted in a selection tool to select the optimal gazing angle(s). For example, patient 4 had a T2 equatorial tumor. The optimization applied for the straight gazing angle resulted in objective function values of 0.46 (iso‐weighted situation), 0.90 (optic nerve prioritization) and 0.08 (macula prioritization) demonstrating the impact of that angle in different clinical approaches. Conclusions The feasibility and suitability of a 3D MRI‐based treatment planning approach have been successfully tested on a cohort of eight patients diagnosed with UM. Moreover, a gaze‐angle trade‐off dose optimization with respect to OARs sparing has been developed. Further validation of the whole treatment process is the next step in the goal to achieve both a non‐invasive and a personalized proton therapy treatment.

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“…Following the landmark-based registration described in [15, 18], we detect the center of the VH, the center of the lens and the optic disc and use this information to align the eyes to a common coordinate system. This pre-processing allows us to form a coherent dataset representing the same anatomical regions across subjects.…”

Section: Methodsmentioning

confidence: 99%

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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Landmark Detection for Fusion of Fundus and MRI Toward a Patient-Specific Multimodal Eye Model

Cited by 11 publications

References 28 publications

Real-Time MRI Reveals Unique Insight into the Full Kinematics of Eye Movements

Real-Time MRI Reveals Unique Insight into the Full Kinematics of Eye Movements

Three‐dimensional MRI‐based treatment planning approach for non‐invasive ocular proton therapy

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

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