Ruggeri Marco, Tsechpenakis Gavriil, Jiao Shuliang, Jockovich Maria Elena, Cebulla Colleen, Hernandez Eleut, Murray Timothy G, Puliafito Carmen A
Bascom Palmer Eye Institute, University of Miami Miller School of Medicine 1638 NW 10th Ave. Miami, FL 33136, USA.
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.
我们使用专为小动物视网膜成像设计的超高分辨率光谱域光学相干断层扫描（SD-OCT），成功地对小鼠模型中的视网膜肿瘤进行了成像。为了分割肿瘤边界并计算肿瘤体积，我们开发了一种新颖的分割算法。该算法基于参数化可变形模型（活动轮廓），并由基于机器学习的区域分类驱动，即条件随机场。通过该算法，我们能够自动获得肿瘤边界，而用户可以在需要时指定额外的约束条件（边界上的点）来校正分割结果。该系统和算法已成功应用于视网膜母细胞瘤小鼠模型中视网膜肿瘤进展的研究以及定量监测治疗效果。
