51 research outputs found
A Depth Based Approach to Glaucoma Detection Using Retinal Fundus Images
Qualitative evaluation of stereo retinal fundus images by experts is a widely accepted method for optic nerve head evaluation (ONH) in glaucoma. The quantitative evaluation using stereo involves depth estimation of the ONH and thresholding of depth to extract optic cup. In this paper, we attempt the reverse, by estimating the disc depth using supervised and unsupervised techniques on a single optic disc image. Our depth estimation approach is evaluated on the INSPIRE-stereo dataset by using single images from the stereo pairs, and is compared with the OCT based depth ground truths. We extract spatial and intensity features from the depth maps, and perform classification of images into glaucomatous and normal. Our approach is evaluated on a dataset of 100 images and achieves an AUC of 0.888 with a sensitivity of 83% at specificity 83%. Experiments indicate that our approach can reliably estimate depth, and provide valuable information for glaucoma detection and for monitoring its progression
A Polar Map Based Approach Using Retinal Fundus Images for Glaucoma Detection
Cup-to-disc ratio is commonly used as an important parameter for glaucoma screening, involving segmentation of the optic cup on fundus images. We propose a novel polar map representation of the optic disc, using a combination of supervised and unsupervised cup segmentation techniques, for detection of glaucoma. Instead of performing hard thresholding on the segmentation output to extract the cup, we consider the cup confidence scores inside the disc to construct a polar map, and extract sector-wise features for learning a glaucoma risk probability (GRP) for the image. We compare the performance of GRP vis-Ã -vis the cup-to-disc ratio (CDR). On an evaluation dataset of 100 images from the publicly available RIM-ONE database, our method achieves 82% sensitivity at 84% specificity, and 96% sensitivity at 60% specificity (AUC of 0.8964). Experiments indicate that the polar map based method can provide a more discriminatory glaucoma risk probability score compared to CDR
A Study of Representational Properties of Unsupervised Anomaly Detection in Brain MRI
Anomaly detection in MRI is of high clinical value in imaging and diagnosis.
Unsupervised methods for anomaly detection provide interesting formulations
based on reconstruction or latent embedding, offering a way to observe
properties related to factorization. We study four existing modeling methods,
and report our empirical observations using simple data science tools, to seek
outcomes from the perspective of factorization as it would be most relevant to
the task of unsupervised anomaly detection, considering the case of brain
structural MRI. Our study indicates that anomaly detection algorithms that
exhibit factorization related properties are well capacitated with delineatory
capabilities to distinguish between normal and anomaly data. We have validated
our observations in multiple anomaly and normal datasets.Comment: Accepted at MICCAI Medical Applications with Disentanglements (MAD)
Workshop 2022 https://mad.ikim.nrw
Generalizable Deep Learning Method for Suppressing Unseen and Multiple MRI Artifacts Using Meta-learning
Magnetic Resonance (MR) images suffer from various types of artifacts due to
motion, spatial resolution, and under-sampling. Conventional deep learning
methods deal with removing a specific type of artifact, leading to separately
trained models for each artifact type that lack the shared knowledge
generalizable across artifacts. Moreover, training a model for each type and
amount of artifact is a tedious process that consumes more training time and
storage of models. On the other hand, the shared knowledge learned by jointly
training the model on multiple artifacts might be inadequate to generalize
under deviations in the types and amounts of artifacts. Model-agnostic
meta-learning (MAML), a nested bi-level optimization framework is a promising
technique to learn common knowledge across artifacts in the outer level of
optimization, and artifact-specific restoration in the inner level. We propose
curriculum-MAML (CMAML), a learning process that integrates MAML with
curriculum learning to impart the knowledge of variable artifact complexity to
adaptively learn restoration of multiple artifacts during training. Comparative
studies against Stochastic Gradient Descent and MAML, using two cardiac
datasets reveal that CMAML exhibits (i) better generalization with improved
PSNR for 83% of unseen types and amounts of artifacts and improved SSIM in all
cases, and (ii) better artifact suppression in 4 out of 5 cases of composite
artifacts (scans with multiple artifacts).Comment: 5 pages, 6 figures, Accepted in EMBC 202
Generalizing Supervised Deep Learning MRI Reconstruction to Multiple and Unseen Contrasts using Meta-Learning Hypernetworks
Meta-learning has recently been an emerging data-efficient learning technique
for various medical imaging operations and has helped advance contemporary deep
learning models. Furthermore, meta-learning enhances the knowledge
generalization of the imaging tasks by learning both shared and discriminative
weights for various configurations of imaging tasks. However, existing
meta-learning models attempt to learn a single set of weight initializations of
a neural network that might be restrictive for multimodal data. This work aims
to develop a multimodal meta-learning model for image reconstruction, which
augments meta-learning with evolutionary capabilities to encompass diverse
acquisition settings of multimodal data. Our proposed model called KM-MAML
(Kernel Modulation-based Multimodal Meta-Learning), has hypernetworks that
evolve to generate mode-specific weights. These weights provide the
mode-specific inductive bias for multiple modes by re-calibrating each kernel
of the base network for image reconstruction via a low-rank kernel modulation
operation. We incorporate gradient-based meta-learning (GBML) in the contextual
space to update the weights of the hypernetworks for different modes. The
hypernetworks and the reconstruction network in the GBML setting provide
discriminative mode-specific features and low-level image features,
respectively. Experiments on multi-contrast MRI reconstruction show that our
model, (i) exhibits superior reconstruction performance over joint training,
other meta-learning methods, and context-specific MRI reconstruction methods,
and (ii) better adaptation capabilities with improvement margins of 0.5 dB in
PSNR and 0.01 in SSIM. Besides, a representation analysis with U-Net shows that
kernel modulation infuses 80% of mode-specific representation changes in the
high-resolution layers. Our source code is available at
https://github.com/sriprabhar/KM-MAML/.Comment: Accepted for publication in Elsevier Applied Soft Computing Journal,
36 pages, 18 figure
Integrated approach for accurate localization of optic disc and macula
The location of three main anatomical structures in the retina namely the optic disc, the vascular arch, and the macula is significant for the analysis of retinal images. Presented here is a novel method that uses an integrated approach to automatically localize the optic disc and the macula with very high accuracy even in the presence of confounders such as lens artifacts, glare, bright pathologies and acquisition variations such as non-uniform illumination, blur and poor contrast. Evaluated on a collective set of 579 diverse pathological images from various publicly available datasets, our method achieves sensitivity > 99% and normalized localization error < 5% for optic disc and macula localization
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