Abstract

Early detection and treatment of glaucoma is of interest as it is a chronic eye disease leading to an irreversible loss of vision. Existing automated systems rely largely on fundus images for assessment of glaucoma due to their fast acquisition and costeffectiveness. Optical Coherence Tomographic (OCT) images provide vital and unambiguous information about nerve fiber loss and optic cup morphology, which are essential for disease assessment. However, the high cost of OCT is a deterrent for deployment in screening at large scale. In this article, we present a novel CAD solution wherein both OCT and fundus modality images are leveraged to learn a model that can perform a mapping of fundus to OCT feature space. We show how this model can be subsequently used to detect glaucoma given an image from only one modality (fundus). The proposed model has been validated extensively on four public andtwo private datasets. It attained an AUC/Sensitivity value of 0.9429/0.9044 on a diverse set of 568 images, which is superior to the figures obtained by a model that is trained only on fundus features. Cross-validation was also done on nearly 1,600 images drawn from a private (OD-centric) and a public (macula-centric) dataset and the proposed model was found to outperform the state-of-the-art method by 8% (public) to 18% (private). Thus, we conclude that fundus to OCT feature space mapping is an attractive option for glaucoma detection.

Abstract

Self-supervised learning is emerging as an effective substitute for transfer learning from large datasets. In this work, we use kidney segmentation to explore this idea. The anatomical asymmetry of kidneys is leveraged to define an effective proxy task for kidney segmentation via self-supervised learning. A siamese convolutional neural network (CNN) is used to classify a given pair of kidney sections from CT volumes as being kidneys of the same or different sides. This knowledge is then transferred for the segmentation of kidneys using another deep CNN using one branch of the siamese CNN as the encoder for the segmentation network. Evaluation results on a publicly available dataset containing computed tomography (CT) scans of the abdominal region shows that a boost in performance and fast convergence can be had relative to a network trained conventionally from scratch. This is notable given that no additional data/expensive annotations or augmentation were used in training.

Abstract

Anatomical variabilities seen in longitudinal data or inter-subject data is usually described by the underlying deformation, captured by non-rigid registration of these images. Stationary Velocity Field (SVF) based non-rigid registration algorithms are widely used for registration. SVF based methods form a metric-free framework which captures a finite dimensional submanifold of deformations embedded in the infinite dimensional smooth manifold of diffeomorphisms. However, these methods cover only a limited degree of deformations. In this paper, we address this limitation and define an approximate metric space for the manifold of diffeomorphisms . We propose a method to break down the large deformation into finite compositions of small deformations. This results in a broken geodesic path on and its length now forms an approximate registration metric. We illustrate the method using a simple, intensity-based, log-demon implementation. Validation results of the proposed method show that it can capture large and complex deformations while producing qualitatively better results than the state-of-the-art methods. The results also demonstrate that the proposed registration metric is a good indicator of the degree of deformation.

Abstract

Disease diagnosis often requires segmentation of structures from a given image followed by shape analysis. Shape analysis entails quantifying the variability in a shape by constructing a template for a given population. We propose an orientation-invariant representation using varifolds for the shape elements in a given shape population and present a novel diffeomorphic Log-demons based template creation pipeline. The proposed method generates a good quality template at a significantly less computation time compared to state of the art method.

Abstract

Deep learning based approaches to Computer Aided Diagnosis (CAD) typically pose the problem as an image classification (Normal or Abnormal) problem. These systems achieve high to very high accuracy in specific disease detection for which they are trained but lack in terms of an explanation for the provided decision/classification result. The activation maps which correspond to decisions do not correlate well with regions of interest for specific diseases. This paper examines this problem and proposes an approach which mimics the clinical practice of looking for an evidence prior to diagnosis. A CAD model is learnt using a mixed set of information: class labels for the entire training set of images plus a rough localisation of suspect regions as an extra input for a smaller subset of training images for guiding the learning. The proposed approach is illustrated with detection of diabetic macular edema (DME) from …

Abstract

This work explores a hybrid approach to segmentation as an alternative to a purely data-driven approach. We introduce an end-to-end U-Net based network called DU-Net, which uses additional frequency preserving features, namely the Scattering Coefficients (SC), for medical image segmentation. SC are translation invariant and Lipschitz continuous to deformations which help DU-Net outperform other conventional CNN counterparts on four datasets and two segmentation tasks: Optic Disc and Optic Cup in color fundus images and fetal Head in ultrasound images. The proposed method shows remarkable improvement over the basic U-Net with performance competitive to state-of-the-art methods. The results indicate that it is possible to use a lighter network trained with fewer images (without any augmentation) to attain good segmentation results.

Abstract

Fingerprint is a common biometric used for authentication and verification of an individual. These images are degraded when fingers are wet, dirty, dry or wounded and due to the failure of the sensors, etc. The extraction of the fingerprint from a degraded image requires denoising and inpainting. We propose to address these problems with an endto-end trainable Convolutional Neural Network based architecture called FPD-M-net, by posing the fingerprint denoising and inpainting problem as a segmentation (foreground) task. Our architecture is based on the M-net with a change: structure similarity loss function, used for better extraction of the fingerprint from the noisy background. Our method outperforms the baseline method and achieves an overall 3rd rank in the Chalearn LAP Inpainting Competition Track 3 −Fingerprint Denoising and Inpainting, ECCV 2018.

Abstract

: A brain magnetic resonanace imaging (MRI) atlas plays an important role in many neuroimage analysis tasks as it provides an atlas with a standard coordinate system which is needed for spatial normalization of a brain MRI. Ideally, this atlas should be as near to the average brain of the population being studied as possible.

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.