Abstract
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 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, 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.
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Notes
- 1.
This treatment has been developed at the Lausanne University Hospital (CHUV) in collaboration with the Jules Gonin University Eye Hospital. It reduces the risk of long term complications in external beam radiation therapy[12].
- 2.
By manual volume definition we mean the manual segmentation of organs at risk and tumor, when possible, in the CT that defines the treatment planning. However, in some cases, the gross tumor volume cannot be manually segmented since it is not visible.
- 3.
The type of registration process used in[7] for adapting the model to the 3D images was not explicitly mentioned in that paper.
- 4.
Every point is represented by a cross and tags enumerating it. However since for this mesh we have a lot of points the tags appear all superposed and make the visualization a little strange.
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Acknowledgements
This work is supported by the Centre d ́Imagerie BioMédicale (CIBM) of the University of Lausanne (UNIL), the Swiss Federal Institute of Technology Lausanne (EPFL), the University of Geneva (UniGe), the Centre Hospitalier Universitaire Vaudois (CHUV), the Hôpitaux Universitaires de Genève (HUG) and the Leenaards and the Jeantet Foundations.
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Cuadra, M.B. et al. (2011). Model-Based Segmentation and Fusion of 3D Computed Tomography and 3D Ultrasound of the Eye for Radiotherapy Planning. In: Tavares, J., Jorge, R. (eds) Computational Vision and Medical Image Processing. Computational Methods in Applied Sciences, vol 19. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0011-6_14
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