Glaucoma is a serious ocular disorder for which the screening and diagnosis are carried out by the examination of the optic nerve head (ONH). The color fundus image (CFI) is the most common modality used for ocular screening. In CFI, the central region which is the optic disc and the optic cup region within the disc are examined to determine one of the important cues for glaucoma diagnosis called the optic cup-to-disc ratio (CDR). CDR calculation requires accurate segmentation of optic disc and cup. Another important cue for glaucoma progression is the variation of depth in ONH region. In this paper, we first propose a deep learning framework to estimate depth from a single fundus image. For the case of monocular retinal depth estimation, we are also plagued by the labeled data insufficiency. To overcome this problem we adopt the technique of pretraining the deep network where, instead of using a denoising autoencoder, we propose a new pretraining scheme called pseudo-depth reconstruction, which serves as a proxy task for retinal depth estimation. Empirically, we show pseudo-depth reconstruction to be a better proxy task than denoising. Our results outperform the existing techniques for depth estimation on the INSPIRE dataset. To extend the use of depth map for optic disc and cup segmentation, we propose a novel fully convolutional guided network, where, along with the color fundus image the network uses the depth map as a guide. We propose a convolutional block called multimodal feature extraction block to extract and fuse the features of the color image and the guide image. We extensively evaluate the proposed segmentation scheme on three datasets- ORIGA, RIMONEr3, and DRISHTI-GS. The performance of the method is comparable and in many cases, outperforms the most recent state of the art.
Very recently, there have been several works based on deep learning for OD and OC segmentation. In @cite_26 , the authors proposed a method where convolutional neural networks (CNNs) are used to learn filters in a greedy manner and then used them for feature extraction following which they obtained pixelwise predictions and final segmentation map using graph cut and convex hull transformations. The network contains fully connected layers and they are also not end-to-end, because of the pipeline of different steps involved. In @cite_30 , the authors proposed a fully convolutional end-to-end OD-OC segmentation method. Recently, in @cite_1 the authors proposed a multiscale network based on a modified U-net @cite_12 architecture for OD-OC segmentation. They employed polar transformation (PT) on the RGB fundus images and segmentation map, before feeding the images to the network and they finally used inverse PT on the output. State of the art results were reported using PT but the network is not end-to-end. We proposed a framework to first estimate depth from a single retinal image and then used an end-to-end network to perform multimodal fusion of features, i.e., combining depth and color image features.
@cite_30 @cite_26 @cite_1 @cite_12
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Glaucoma is a highly threatening and widespread ocular disease which may lead to permanent loss in vision. One of the important parameters used for Glaucoma screening in the cup-to-disc ratio (CDR), which requires accurate segmentation of optic cup and disc. We explore fully convolutional networks (FCNs) for the task of joint segmentation of optic cup and disc. We propose a novel improved architecture building upon FCNs by using the concept of residual learning. Additionally, we also explore if adversarial training helps in improving the segmentation results. The method does not require any complicated preprocessing techniques for feature enhancement. We learn a mapping between the retinal images and the corresponding segmentation map using fully convolutional and adversarial networks. We perform extensive experiments of various models on a set of 159 images from RIM-ONE database and also do extensive comparison. The proposed method outperforms the state of the art methods on various evaluation metrics for both disc and cup segmentation. Abstract We present a novel method to segment retinal images using ensemble learning based convolutional neural network (CNN) architectures. An entropy sampling technique is used to select informative points thus reducing computational complexity while performing superior to uniform sampling. The sampled points are used to design a novel learning framework for convolutional filters based on boosting. Filters are learned in several layers with the output of previous layers serving as the input to the next layer. A softmax logistic classifier is subsequently trained on the output of all learned filters and applied on test images. The output of the classifier is subject to an unsupervised graph cut algorithm followed by a convex hull transformation to obtain the final segmentation. Our proposed algorithm for optic cup and disc segmentation outperforms existing methods on the public DRISHTI-GS data set on several metrics. Glaucoma is a chronic eye disease that leads to irreversible vision loss. The cup to disc ratio (CDR) plays an important role in the screening and diagnosis of glaucoma. Thus, the accurate and automatic segmentation of optic disc (OD) and optic cup (OC) from fundus images is a fundamental task. Most existing methods segment them separately, and rely on hand-crafted visual feature from fundus images. In this paper, we propose a deep learning architecture, named M-Net, which solves the OD and OC segmentation jointly in a one-stage multi-label system. The proposed M-Net mainly consists of multi-scale input layer, U-shape convolutional network, side-output layer, and multi-label loss function. The multi-scale input layer constructs an image pyramid to achieve multiple level receptive field sizes. The U-shape convolutional network is employed as the main body network structure to learn the rich hierarchical representation, while the side-output layer acts as an early classifier that produces a companion local prediction map for different scale layers. Finally, a multi-label loss function is proposed to generate the final segmentation map. For improving the segmentation performance further, we also introduce the polar transformation, which provides the representation of the original image in the polar coordinate system. The experiments show that our M-Net system achieves state-of-the-art OD and OC segmentation result on ORIGA data set. Simultaneously, the proposed method also obtains the satisfactory glaucoma screening performances with calculated CDR value on both ORIGA and SCES datasets. There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http: lmb.informatik.uni-freiburg.de people ronneber u-net .