Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A Novel Statistical Shape Model for Treatment Planning of Retinoblastoma

Purpose: Proper delineation of ocular anatomy in 3-dimensional (3D) imaging is a big challenge, particularly when developing treatment plans for ocular diseases. Magnetic resonance imaging (MRI) is presently used in clinical practice for diagnosis confirmation and treatment planning for treatment of retinoblastoma in infants, where it serves as a source of information, complementary to the fundus or ultrasonographic imaging. Here we present a framework to fully automatically segment the eye anatomy for MRI based on 3D active shape models (ASM), and we validate the results and present a proof of concept to automatically segment pathological eyes. Methods and Materials: Manual and automatic segmentation were performed in 24 images of healthy children's eyes (3.29 +/- 2.15 years of age). Imaging was performed using a 3-T MRI scanner. The ASM consists of the lens, the vitreous humor, the sclera, and the cornea. The model was fitted by first automatically detecting the position of the eye center, the lens, and the optic nerve, and then aligning the model and fitting it to the patient. We validated our segmentation method by using a leave-one-out cross- validation. The segmentation results were evaluated by measuring the overlap, using the Dice similarity coefficient (DSC) and the mean distance error. Results: We obtained a DSC of 94.90 +/- 2.12% for the sclera and the cornea, 94.72 +/- 1.89% for the vitreous humor, and 85.16 +/- 4.91% for the lens. The mean distance error was 0.26 +/- 0.09 mm. The entire process took 14 seconds on average per eye. Conclusion: We provide a reliable and accurate tool that enables clinicians to automatically segment the sclera, the cornea, the vitreous humor, and the lens, using MRI. We additionally present a proof of concept for fully automatically segmenting eye pathology. This tool reduces the time needed for eye shape delineation and thus can help clinicians when planning eye treatment and confirming the extent of the tumor. (C) 2015 Elsevier Inc. All rights reserved.

Personalized Anatomic Eye Model From T1-Weighted Volume Interpolated Gradient Echo Magnetic Resonance Imaging of Patients With Uveal Melanoma

Meritxell Bach Cuadra, Alessia Pica, Raphael Sznitman

Purpose: We present a 3-dimensional patient-specific eye model from magnetic resonance imaging (MRI) for proton therapy treatment planning of uveal melanoma (UM). During MRI acquisition of UM patients, the point fixation can be difficult and, together with physiological blinking, can introduce motion artifacts in the images, thus challenging the model creation. Furthermore, the unclear boundary of the small objects (eg, lens, optic nerve) near the muscle or of the tumors with hemorrhage and tantalum clips can limit model accuracy. Methods and Materials: A dataset of 37 subjects, including 30 healthy eyes of volunteers and 7 eyes of UM patients, was investigated. In our previous work, active shape model was successfully applied to retinoblastoma eye segmentation in T1-weighted 3T MRI. Here, we evaluate this method in a more challenging setting, based on 1.5T MRI acquisition and different datasets of awake adult eyes with UM. The lens and cornea together with the sclera, vitreous humor, and optic nerve were automatically segmented and validated against manual delineations of a senior ocular radiation oncologist, in terms of the Dice similarity coefficient and Hausdorff distance. Results: Leave-one-out cross validation (mixing both volunteers and UM patients) yielded median Dice similarity coefficient values (respective of Hausdorff distance) of 94.5% (1.64 mm) for the sclera, 92.2% (1.73 mm) for the vitreous humor, 88.3% (1.09 mm) for the lens, and 81.9% (1.86 mm) for the optic nerve. The average computation time for an eye was 10 seconds. Conclusions: To our knowledge, our work is the first attempt to automatically segment adult eyes, including patients with UM. Our results show that automated active shape model segmentation can succeed in the presence of motion, tumors, and tantalum clips. These results are promising for inclusion in clinical practice. (C) 2018 Elsevier Inc. All rights reserved.

ELSEVIER SCIENCE INC2018

Model-based Segmentation and Image Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Meritxell Bach Cuadra, Alessia Pica, Jean-Philippe Thiran, Subrahmanyam Venkata Ravi Mohana Sai Gorthi

For radiotherapy treatment planning of retinoblastoma in childhood, Computed Tomography (CT) represents the standard method for tumor volume delineation, despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a high accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both. In this paper, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentations. Then, for the fusion of 3D CT and US images, we present two approaches: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

2009

Model-based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning

Meritxell Bach Cuadra, Fikret Isik Karahanoglu, Alessia Pica, Jean-Philippe Thiran, Subrahmanyam Venkata Ravi Mohana Sai Gorthi

Computed Tomography (CT) represents the standard imaging modality for tumor volume delineation for radiotherapy treatment planning of retinoblastoma despite some inherent limitations. CT scan is very useful in providing information on physical density for dose calculation and morphological volumetric information but presents a low sensitivity in assessing the tumor viability. On the other hand, 3D ultrasound (US) allows a highly accurate definition of the tumor volume thanks to its high spatial resolution but it is not currently integrated in the treatment planning but used only for diagnosis and follow-up. Our ultimate goal is an automatic segmentation of gross tumor volume (GTV) in the 3D US, the segmentation of the organs at risk (OAR) in the CT and the registration of both modalities. In this chapter, we present some preliminary results in this direction. We present 3D active contour-based segmentation of the eye ball and the lens in CT images; the presented approach incorporates the prior knowledge of the anatomy by using a 3D geometrical eye model. The automated segmentation results are validated by comparing with manual segmentation. Then, we present two approaches for the fusion of 3D CT and US images: (i) landmark-based transformation, and (ii) object-based transformation that makes use of eye ball contour information on CT and US images.

Springer2011

Personalized Anatomic Eye Model From T1-Weighted Volume Interpolated Gradient Echo Magnetic Resonance Imaging of Patients With Uveal Melanoma

Meritxell Bach Cuadra, Alessia Pica, Raphael Sznitman

ELSEVIER SCIENCE INC2018