598 research outputs found
Optic nerve head segmentation
Reliable and efficient optic disk localization and segmentation are important tasks in automated retinal screening. General-purpose edge detection algorithms often fail to segment the optic disk due to fuzzy boundaries, inconsistent image contrast or missing edge features. This paper presents an algorithm for the localization and segmentation of the optic nerve head boundary in low-resolution images (about 20 /spl mu//pixel). Optic disk localization is achieved using specialized template matching, and segmentation by a deformable contour model. The latter uses a global elliptical model and a local deformable model with variable edge-strength dependent stiffness. The algorithm is evaluated against a randomly selected database of 100 images from a diabetic screening programme. Ten images were classified as unusable; the others were of variable quality. The localization algorithm succeeded on all bar one usable image; the contour estimation algorithm was qualitatively assessed by an ophthalmologist as having Excellent-Fair performance in 83% of cases, and performs well even on blurred image
A ResNet is All You Need? Modeling A Strong Baseline for Detecting Referable Diabetic Retinopathy in Fundus Images
Deep learning is currently the state-of-the-art for automated detection of
referable diabetic retinopathy (DR) from color fundus photographs (CFP). While
the general interest is put on improving results through methodological
innovations, it is not clear how good these approaches perform compared to
standard deep classification models trained with the appropriate settings. In
this paper we propose to model a strong baseline for this task based on a
simple and standard ResNet-18 architecture. To this end, we built on top of
prior art by training the model with a standard preprocessing strategy but
using images from several public sources and an empirically calibrated data
augmentation setting. To evaluate its performance, we covered multiple
clinically relevant perspectives, including image and patient level DR
screening, discriminating responses by input quality and DR grade, assessing
model uncertainties and analyzing its results in a qualitative manner. With no
other methodological innovation than a carefully designed training, our ResNet
model achieved an AUC = 0.955 (0.953 - 0.956) on a combined test set of 61007
test images from different public datasets, which is in line or even better
than what other more complex deep learning models reported in the literature.
Similar AUC values were obtained in 480 images from two separate in-house
databases specially prepared for this study, which emphasize its generalization
ability. This confirms that standard networks can still be strong baselines for
this task if properly trained.Comment: Accepted for publication at the 18th International Symposium on
Medical Information Processing and Analysis (SIPAIM 2022
OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing
Non-mydriatic retinal color fundus photography (CFP) is widely available due
to the advantage of not requiring pupillary dilation, however, is prone to poor
quality due to operators, systemic imperfections, or patient-related causes.
Optimal retinal image quality is mandated for accurate medical diagnoses and
automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to
propose an unpaired image-to-image translation scheme for mapping low-quality
retinal CFPs to high-quality counterparts. Furthermore, to improve the
flexibility, robustness, and applicability of our image enhancement pipeline in
the clinical practice, we generalized a state-of-the-art model-based image
reconstruction method, regularization by denoising, by plugging in priors
learned by our OT-guided image-to-image translation network. We named it as
regularization by enhancing (RE). We validated the integrated framework, OTRE,
on three publicly available retinal image datasets by assessing the quality
after enhancement and their performance on various downstream tasks, including
diabetic retinopathy grading, vessel segmentation, and diabetic lesion
segmentation. The experimental results demonstrated the superiority of our
proposed framework over some state-of-the-art unsupervised competitors and a
state-of-the-art supervised method.Comment: Accepted as a conference paper to The 28th biennial international
conference on Information Processing in Medical Imaging (IPMI 2023
- …