Abstract

Fundus imaging with a Smartphone camera (SC) is a cost effective solution for the assessment of retina. However, imaging at high magnification and low light levels, results in loss of details, uneven illumination and noise especially in the peripheral region. We address these problems by matching the characteristics of images from SC to those from a regular fundus camera (FC) with an architecture called ResCycleGAN. It is based on the CycleGAN with two significant changes: A residual connection is introduced to aid learning only the correction required; A structure similarity based loss function is used to improve the clarity of anatomical structures and pathologies. The proposed method can handle variations seen in normal and pathological images, acquired even without mydriasis, which is attractive in screening. The method produces consistently balanced results, outperforms CycleGAN both qualitatively and quantitatively, and has more pleasing results.

Abstract

The Optic Disc (OD) and Optic Cup (OC) boundaries play a critical role in the detection of glaucoma. However, very few annotated datasets are available for both OD and OC that are required for segmentation. Recently, Convolutional Neural Networks have shown significant improvements in segmentation performance. However, the full potential of CNNs is hindered by the lack of a large amount of annotated training images. To address this issue, we explore a method to generate synthetic images which can be used to augment the training data. Given the segmentation masks of OD, OC and vessels from arbitrarily different fundus images, the proposed method employs a combination of B-spline registration and GAN to generate high quality images that ensure that the vessels bend at the edge of the OC in a realistic manner. In contrast, the existing GAN based methods for fundus image synthesis fail to capture the local details and vasculature in the Optic Nerve Head (ONH) region. The utility of the proposed method in training deep networks for the challenging problem of OC segmentation is explored and an improvement in the dice score from 0.85 to 0.902 is seen with the inclusion of the synthetic images in the training set.

Abstract

Optical coherence tomographic (OCT) images provide valuable information for understanding the changes occurring in the retina due to glaucoma, specifically, related to the retinal nerve fiber layer and the optic nerve head. In this paper, we propose a deep learning approach using Capsule network for glaucoma classification, which directly operates on 3D OCT volumes. The network is trained only on labelled volumes and does not attempt any region/structure segmentation. The proposed network was assessed on 50 volumes and found to achieve 0.97 for the area under the ROC curve (AUC). This is considerably higher than the existing approaches which are majorly based on machine learning or rely on segmentation of the required structures from OCT. Our network also outperforms 3D convolutional neural networks despite the fewer network parameters and fewer epochs needed for training.

Abstract

Automated and accurate segmentation of cystoid structures in Optical Coherence Tomography (OCT) is of interest in the early detection of retinal diseases. It is however a challenging task. We propose a novel method for localizing cysts in 3D OCT volumes. The proposed work is biologically inspired and based on selective enhancement of the cysts, by inducing motion to a given OCT slice. A Convolutional Neural Network (CNN) is designed to learn a mapping function that combines the result of multiple such motions to produce a probability map for cyst locations in a given slice. The final segmentation of cysts is obtained via simple clustering of the detected cyst locations. The proposed method is evaluated on two public datasets and one private dataset. The public datasets include the one released for the OPTIMA Cyst segmentation challenge (OCSC) in MICCAI 2015 and the DME dataset. After training on the OCSC train set, the method achieves a mean Dice Coefficient (DC) of 0.71 on the OCSC test set. The robustness of the algorithm was examined by cross validation on the DME and AEI (private) datasets and a mean DC values obtained were 0.69 and 0.79, respectively. Overall, the proposed system outperforms all benchmarks. These results underscore the strengths of the proposed method in handling variations in both data acquisition protocols and scanners.

Abstract

Purpose To analyse the expansion of radial peripapillary capillary (RPC) network with optical coherence tomography angiography (OCT-A) in normal human eyes and correlate RPC density with retinal nerve fibre layer thickness (RNFLT) at various distances from the optic nerve head (ONH) edge. Methods Fifty eyes of 50 healthy subjects underwent imaging with RTVue XR-100 Avanti OCT. OCT-A scans of Angio disc (6 9 6 mm) and Angio retina (8 9 8 mm) were combined to create a wide-field montage image of the RPC network. RPC density and RNFLT was calculated at different circle diameter around the ONH, and their correlation was measured. Results In the arcuate region, RPC was detected as far as 8.5 mm from the ONH edge, but not around the perifoveal area within 0.025 ± 0.01 mm2.The mean RPC density (0.1556 ± 0.015) and RNFLT (245.96 ± 5.79) were highest at 1.5 mm from ONH margin, and there was a trend in its decline, in a distance-dependent manner, with the least density at 8.5 mm (all P\0.0001). Highest RPC density was noted in the arcuate fibre region at all the distances. Overall mean RPC density correlated significantly (P\0.0001) with the overall mean RNFLT. Conclusions Wide-field montage OCT-A angiograms can visualize expansion of the RPC network, which is useful in obtaining information about various retinal disorders. The results obtained support the hypothesis that the RPC network could be responsible for RNFL nourishment.

Abstract

Optical Coherence Tomography (OCT) plays an important role in the analysis of retinal diseases such as Age-Related Macular Degeneration (AMD). In this paper, we present a method to construct a normative atlas for macula centric OCT volumes with a mean intensity template (MT) and probabilistic maps for the seven intra-retinal tissue layers. We also propose an AMD classification scheme where the deviation of the local similarity of a test volume with respect to the MT is used to characterize AMD. The probabilistic atlas was used for layer segmentation where we achieved an average dice score of 0.82 across the eight layer boundaries. On the AMD detection task, the classification accuracy and Area under the Receiver Operating Characteristic curve were 98% and 0.996 respectively, on 170 OCT test volumes.

Abstract

Automatic disease detection and classification have been attracting much interest. High performance is critical in adoption of such systems, which generally rely on training with a wide variety of annotated data. Availability of such varied annotated data in medical imaging is very scarce. Synthetic data generation is a promising solution to address this problem. We propose a novel method, based on generative adversarial networks (GAN), to generate images with lesions such that the overall severity level can be controlled. We demonstrate the reliability of the generated synthetic images independently as well as by training a computer aided diagnosis (CAD) system with the generated data. We showcase this approach for heamorrhage detection in retinal images with 4 levels of severity. Quantitative assessment results show that the generated synthetic images are very close to the real data. Haemorrhage detection was found to improve with inclusion of synthetic data in the training set with improvements in sensitivity ranging from 20% to 27% over training with just expert marked data.

Abstract

The level set based deformable models (LDM) are commonly used for medical image segmentation. However, they rely on a handcrafted curve evolution velocity that needs to be adapted for each segmentation task. The Convolutional Neural Networks (CNN) address this issue by learning robust features in a supervised end-to-end manner. However, CNNs employ millions of network parameters, which require a large amount of data during training to prevent over-fitting and increases the memory requirement and computation time during testing. Moreover, since CNNs pose segmentation as a region-based pixel labeling, they cannot explicitly model the high-level dependencies between the points on the object boundary to preserve its overall shape, smoothness or the regional homogeneity within and outside the boundary. We present a Recurrent Neural Network based solution called the RACE-net to address the above issues. RACE-net models a generalized LDM evolving under a constant and mean curvature velocity. At each time-step, the curve evolution velocities are approximated using a feed-forward architecture inspired by the multiscale image pyramid. RACE-net allows the curve evolution velocities to be learned in an end-to-end manner while minimizing the number of network parameters, computation time, and memory requirements. The RACEnet was validated on three different segmentation tasks: optic disc and cup in color fundus images, cell nuclei in histopathological images, and the left atrium in cardiac MRI volumes. Assessment on public datasets was seen to yield high Dice values between 0.87 and 0.97, which illustrates its utility as a generic, off-the-shelf architecture for biomedical segmentation.

Abstract

Feature-based registration has been popular with a variety of features ranging from voxel intensity to Self-Similarity Context (SSC). In this paper, we examine the question of how features learnt using various Deep Learning (DL) frameworks can be used for deformable registration and whether this feature learning is necessary or not. We investigate the use of features learned by different DL methods in the current state-ofthe-art discrete registration framework and analyze its performance on 2 publicly available datasets. We draw insights about the type of DL framework useful for feature learning. We consider the impact, if any, of the complexity of different DL models and brain parcellation methods on the performance of discrete registration. Our results indicate that the registration performance with DL features and SSC are comparable and stable across datasets whereas this does not hold for low level features. This shows that when handcrafted features are designed based on good insights into the problem at hand, they perform better or are comparable to features learnt using deep learning framework.

Abstract

Background and Objective: Accurate segmentation of the intra-retinal tissue layers in Optical Coherence Tomography (OCT) images plays an important role in the diagnosis and treatment of ocular diseases such as Age-Related Macular Degeneration (AMD) and Diabetic Macular Edema (DME). The existing energy minimization based methods employ multiple, manually handcrafted cost terms and often fail in the presence of pathologies. In this work, we eliminate the need to handcraft the energy by learning it from training images in an end-to-end manner. Our method can be easily adapted to pathologies by re-training it on an appropriate dataset. Methods: We propose a Conditional Random Field (CRF) framework for the joint multi-layer segmentation of OCT B-scans. The appearance of each retinal layer and boundary is modeled by two convolutional filter banks and the shape priors are modeled using Gaussian distributions. The total CRF energy is linearly parameterized to allow a joint, end-to-end training by employing the Structured Support Vector Machine.