Retinal tumor imaging and volume quantification in mouse model using spectral-domain optical coherence tomography.

Abstract

We have successfully imaged the retinal tumor in a mouse model using an ultra-high resolution spectral-domain optical coherence tomography (SD-OCT) designed for small animal retinal imaging. For segmentation of the tumor boundaries and calculation of the tumor volume, we developed a novel segmentation algorithm. The algorithm is based on parametric deformable models (active contours) and is driven by machine learning-based region classification, namely a Conditional Random Field. With this algorithm we are able to obtain the tumor boundaries automatically, while the user can specify additional constraints (points on the boundary) to correct the segmentation result, if needed. The system and algorithm were successfully applied to studies on retinal tumor progression and monitoring treatment effects quantitatively in a mouse model of retinoblastoma.

• Authors: Marco Ruggeri, Gavriil Tsechpenakis, Shuliang Jiao, Maria Elena Jockovich, Colleen Cebulla, Eleut Hernandez, Timothy G Murray, Carmen A Puliafito
• Journal: Optics express (Opt Express) Vol. 17 Issue 5 Pg. 4074-83 (Mar 02 2009) ISSN: 1094-4087 [Electronic] United States
• PMID: 19259247 (Publication Type: Journal Article, Research Support, N.I.H., Extramural)

Chemical References

• Cinnamates
• Luteinizing Hormone, beta Subunit
• SU 1498
• Vascular Endothelial Growth Factor Receptor-2

Topics

• Algorithms
• Animals
• Cinnamates (pharmacology)
• Disease Models, Animal
• Image Processing, Computer-Assisted
• Luteinizing Hormone, beta Subunit (genetics)
• Mice
• Mice, Inbred BALB C
• Mice, Transgenic
• Retinal Neoplasms (drug therapy, etiology, pathology)
• Retinoblastoma (drug therapy, etiology, pathology)
• Tomography, Optical Coherence (methods, statistics & numerical data)
• Vascular Endothelial Growth Factor Receptor-2 (antagonists & inhibitors)