Finding Cysts, Part Five: Final Detection
The goal is to automatically detect the appearance of Cystoid Macular Edema (CME) in Optical Coherence Tomography (OCT) images. The deep learning technique used, Convolutional Neural Networks, takes as an input patches of pixels from within the retina. These patches were generated from previous segmentation of retinal images. A further segmentation of the retina is performed using an image processing algorithm called SLIC. Every superpixel thus generated, after being labeled as in the OCT scan, is fed into the neural network to detect the cyst.
Read More
Finding Cysts Part Three: Layer Segmentation
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
Read More
Automatic Detection of Macular Cysts
A series of five articles on our Cysts Detection project using deep learning and Convolutional Neural Networks: 1) our cyst detection method; 2) the cyst denoising process; 3) the retinal layer segmentation; 4) the automatical seed-detection; 5) the final detection of the cysts. Our method is exceptionally successful at finding the cysts themselves and most of their area. Remarkable results are achieved even when using relatively small datasets in the training process.
Read More