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

Opt Express. 2009 Mar 2;17(5):4074-83. doi: 10.1364/oe.17.004074.


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.

Publication types

  • Research Support, N.I.H., Extramural

MeSH terms

  • 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
  • Retinal Neoplasms / etiology
  • Retinal Neoplasms / pathology*
  • Retinoblastoma / drug therapy
  • Retinoblastoma / etiology
  • Retinoblastoma / pathology
  • Tomography, Optical Coherence / methods*
  • Tomography, Optical Coherence / statistics & numerical data
  • Vascular Endothelial Growth Factor Receptor-2 / antagonists & inhibitors


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