The purpose of this project is to develop a retinal imaging system suitable for routine examination or screening of mouse models that acquires fundus and Optical Coherence Tomography (OCT) images. The imaging system is composed of a digital camera with an objective for biomicroscopic examination of the fundus, an OCT interferometer, an OCT beam delivery system designed for the mouse eye, and a mouse positioning stage. The image acquisition is controlled with software that displays the fundus and OCT images in real-time, and allows the user to control the position of the OCT beam spot on the fundus image display. The system was used to image healthy mice and a mouse model of glaucoma. Fundus images and OCT scans were successfully acquired in both eyes of all mice with eyes that had clear optics. The study demonstrates the feasibility of acquiring simultaneous fundus and OCT images of the mouse retina, by a single operator, in a manner suitable for rapid evaluation of mouse models of retinal disease.

Indiana University-Purdue University Indianapolis (IUPUI) / We present a novel, variational and statistical approach for model-based segmentation. Our model generalizes the Chan-Vese model, proposed for concurrent segmentation of multiple objects embedded in the same image domain. We also propose a novel shape descriptor, namely the Multi-Compartment Distance Functions or mcdf. Our proposed framework for segmentation is two-fold: first, several training samples distributed across various classes are registered onto a common frame of reference; then, we use a variational method similar to Active Shape Models (or ASMs) to generate an average shape model and hence use the latter to partition new images. The key advantages of such a framework is: (i) landmark-free automated shape training; (ii) strict shape constrained model to fit test data. Our model can naturally deal with shapes of arbitrary dimension and topology(closed/open curves). We term our model Active Geometric Model, since it focuses on segmentation of geometric shapes. We demonstrate the power of the proposed framework in two important medical applications: one for morphology estimation of 3D Motor Neuron compartments, another for thickness estimation of Henle's Fiber Layer in the retina. We also compare the qualitative and quantitative performance of our method with that of several other state-of-the-art segmentation methods.