Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A novel Statistical Shape Model for treatment planning of Retinoblastoma

Ciller, Carlos; De Zanet, Sandro; Rüegsegger, Michael; Pica, Alessia; Sznitman, Raphael; Thiran, Jean-Philippe; Maeder, Philippe; Munier, Francis L.; Kowal, Horst Jens; Bach Cuadra, Meritxell (2015). Automatic Segmentation of the Eye in 3D Magnetic Resonance Imaging: A novel Statistical Shape Model for treatment planning of Retinoblastoma. International journal of radiation oncology, biology, physics, 92(4), pp. 794-802. Elsevier 10.1016/j.ijrobp.2015.02.056

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Purpose: Proper delineation of ocular anatomy in 3D imaging is a big challenge, particularly when
developing treatment plans for ocular diseases. Magnetic Resonance Imaging (MRI) is nowadays
utilized in clinical practice for the diagnosis confirmation and treatment planning of retinoblastoma in
infants, where it serves as a source of information, complementary to the Fundus or Ultrasound
imaging. Here we present a framework to fully automatically segment the eye anatomy in the MRI
based on 3D Active Shape Models (ASM), we validate the results and present a proof of concept to
automatically segment pathological eyes.

Material and Methods: Manual and automatic segmentation were performed on 24 images of healthy
children eyes (3.29±2.15 years). Imaging was performed using a 3T MRI scanner. The ASM
comprises 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, then aligning the
model and fitting it to the patient. We validated our segmentation method 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±0.09mm. The entire
process took 14s 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 pathological eyes. 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.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Clinic of Radiation Oncology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - AI in Medical Imaging Laboratory
04 Faculty of Medicine > Department of Head Organs and Neurology (DKNS) > Clinic of Ophthalmology

UniBE Contributor:

Ciller, Carlos, De Zanet, Sandro, Rüegsegger, Michael, Pica, Alessia, Sznitman, Raphael, Kowal, Horst Jens


600 Technology > 610 Medicine & health








Sandro De Zanet

Date Deposited:

02 Apr 2015 11:01

Last Modified:

05 Dec 2022 14:44

Publisher DOI:


PubMed ID:





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