Deep learning approaches have been used extensively for medical image segmentation tasks. Training deep networks for segmentation, however, typically requires manually delineated examples which provide a ground truth for optimization of the network. In this work, we present a neural network architecture that segments vascular structures in retinal OCTA images without the need of direct supervision. Instead, we propose a variational intensity cross channel encoder that finds vessel masks by exploiting the common underlying structure shared by two OCTA images of the the same region but acquired on different devices. Experimental results demonstrate significant improvement over three existing methods that are commonly used.
Monitoring retinal thickness of persons with multiple sclerosis (MS) provides important bio-markers for disease progression. However, changes in retinal thickness can be small and concealed by noise in the acquired data. Consistent longitudinal retinal layer segmentation methods for optical coherence tomography (OCT) images are crucial for identifying the real longitudinal retinal changes of individuals with MS. In this paper, we propose an iterative registration and deep learning based segmentation method for longitudinal 3D OCT scans. Since 3D OCT scans are usually anisotropic with large slice separation, we extract B-scan features using 2D deep networks and utilize inter-B-scan context with convolutional long-short-term memory (LSTM). To incorporate longitudinal information, we perform fundus registration and interpolate the smooth retinal surfaces of the previous visit to use as a prior on the current visit.
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