20 research outputs found
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentatio
Learning to Address Intra-segment Misclassification in Retinal Imaging
Accurate multi-class segmentation is a long-standing challenge in medical imaging, especially in scenarios where classes share strong similarity. Segmenting retinal blood vessels in retinal photographs is one such scenario, in which arteries and veins need to be identified and differentiated from each other and from the background. Intra-segment misclassification, i.e. veins classified as arteries or vice versa, frequently occurs when arteries and veins intersect, whereas in binary retinal vessel segmentation, error rates are much lower. We thus propose a new approach that decomposes multi-class segmentation into multiple binary, followed by a binary-to-multi-class fusion network. The network merges representations of artery, vein, and multi-class feature maps, each of which are supervised by expert vessel annotation in adversarial training. A skip-connection based merging process explicitly maintains class-specific gradients to avoid gradient vanishing in deep layers, to favor the discriminative features. The results show that, our model respectively improves F1-score by 4.4%, 5.1%, and 4.2% compared with three state-of-the-art deep learning based methods on DRIVE-AV, LES-AV, and HRF-AV data sets. Code: https://github.com/rmaphoh/Learning-AVSegmentatio
Robust semi-automatic vessel tracing in the human retinal image by an instance segmentation neural network
The morphology and hierarchy of the vascular systems are essential for
perfusion in supporting metabolism. In human retina, one of the most
energy-demanding organs, retinal circulation nourishes the entire inner retina
by an intricate vasculature emerging and remerging at the optic nerve head
(ONH). Thus, tracing the vascular branching from ONH through the vascular tree
can illustrate vascular hierarchy and allow detailed morphological
quantification, and yet remains a challenging task. Here, we presented a novel
approach for a robust semi-automatic vessel tracing algorithm on human fundus
images by an instance segmentation neural network (InSegNN). Distinct from
semantic segmentation, InSegNN separates and labels different vascular trees
individually and therefore enable tracing each tree throughout its branching.
We have built-in three strategies to improve robustness and accuracy with
temporal learning, spatial multi-sampling, and dynamic probability map. We
achieved 83% specificity, and 50% improvement in Symmetric Best Dice (SBD)
compared to literature, and outperformed baseline U-net. We have demonstrated
tracing individual vessel trees from fundus images, and simultaneously retain
the vessel hierarchy information. InSegNN paves a way for any subsequent
morphological analysis of vascular morphology in relation to retinal diseases
Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation
Purpose: Automatic methods of analyzing of retinal vascular networks, such as retinal
blood vessel detection, vascular network topology estimation, and arteries / veins classi cation
are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide
spectrum of diseases.
Methods: We propose a new framework for precisely segmenting retinal vasculatures,
constructing retinal vascular network topology, and separating the arteries and veins. A
non-local total variation inspired Retinex model is employed to remove the image intensity
inhomogeneities and relatively poor contrast. For better generalizability and segmentation
performance, a superpixel based line operator is proposed as to distinguish between lines and
the edges, thus allowing more tolerance in the position of the respective contours. The concept
of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel
network into arteries and veins.
Results: The proposed segmentation method yields competitive results on three pub-
lic datasets (STARE, DRIVE, and IOSTAR), and it has superior performance when com-
pared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964,
respectively. The topology estimation approach has been applied to ve public databases
1
(DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830,
0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries / veins classi cation
based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and
VICAVR) are 0.90.9, 0.910, and 0.907, respectively.
Conclusions: The experimental results show that the proposed framework has e ectively
addressed crossover problem, a bottleneck issue in segmentation and vascular topology recon-
struction. The vascular topology information signi cantly improves the accuracy on arteries
/ veins classi cation
Network-based features for retinal fundus vessel structure analysis
Retinal fundus imaging is a non-invasive method that allows visualizing the structure of the blood vessels in the retina whose features may indicate the presence of diseases such as diabetic retinopathy (DR) and glaucoma. Here we present a novel method to analyze and quantify changes in the retinal blood vessel structure in patients diagnosed with glaucoma or with DR. First, we use an automatic unsupervised segmentation algorithm to extract a tree-like graph from the retina blood vessel structure. The nodes of the graph represent branching (bifurcation) points and endpoints, while the links represent vessel segments that connect the nodes. Then, we quantify structural differences between the graphs extracted from the groups of healthy and non-healthy patients. We also use fractal analysis to characterize the extracted graphs. Applying these techniques to three retina fundus image databases we find significant differences between the healthy and non-healthy groups (p-values lower than 0.005 or 0.001 depending on the method and on the database). The results are sensitive to the segmentation method (manual or automatic) and to the resolution of the images.Peer ReviewedPostprint (published version
The Little W-Net That Could: State-of-the-Art Retinal Vessel Segmentation with Minimalistic Models
The segmentation of the retinal vasculature from eye fundus images represents
one of the most fundamental tasks in retinal image analysis. Over recent years,
increasingly complex approaches based on sophisticated Convolutional Neural
Network architectures have been slowly pushing performance on well-established
benchmark datasets. In this paper, we take a step back and analyze the real
need of such complexity. Specifically, we demonstrate that a minimalistic
version of a standard U-Net with several orders of magnitude less parameters,
carefully trained and rigorously evaluated, closely approximates the
performance of current best techniques. In addition, we propose a simple
extension, dubbed W-Net, which reaches outstanding performance on several
popular datasets, still using orders of magnitude less learnable weights than
any previously published approach. Furthermore, we provide the most
comprehensive cross-dataset performance analysis to date, involving up to 10
different databases. Our analysis demonstrates that the retinal vessel
segmentation problem is far from solved when considering test images that
differ substantially from the training data, and that this task represents an
ideal scenario for the exploration of domain adaptation techniques. In this
context, we experiment with a simple self-labeling strategy that allows us to
moderately enhance cross-dataset performance, indicating that there is still
much room for improvement in this area. Finally, we also test our approach on
the Artery/Vein segmentation problem, where we again achieve results
well-aligned with the state-of-the-art, at a fraction of the model complexity
in recent literature. All the code to reproduce the results in this paper is
released
RVD: A Handheld Device-Based Fundus Video Dataset for Retinal Vessel Segmentation
Retinal vessel segmentation is generally grounded in image-based datasets
collected with bench-top devices. The static images naturally lose the dynamic
characteristics of retina fluctuation, resulting in diminished dataset
richness, and the usage of bench-top devices further restricts dataset
scalability due to its limited accessibility. Considering these limitations, we
introduce the first video-based retinal dataset by employing handheld devices
for data acquisition. The dataset comprises 635 smartphone-based fundus videos
collected from four different clinics, involving 415 patients from 50 to 75
years old. It delivers comprehensive and precise annotations of retinal
structures in both spatial and temporal dimensions, aiming to advance the
landscape of vasculature segmentation. Specifically, the dataset provides three
levels of spatial annotations: binary vessel masks for overall retinal
structure delineation, general vein-artery masks for distinguishing the vein
and artery, and fine-grained vein-artery masks for further characterizing the
granularities of each artery and vein. In addition, the dataset offers temporal
annotations that capture the vessel pulsation characteristics, assisting in
detecting ocular diseases that require fine-grained recognition of hemodynamic
fluctuation. In application, our dataset exhibits a significant domain shift
with respect to data captured by bench-top devices, thus posing great
challenges to existing methods. In the experiments, we provide evaluation
metrics and benchmark results on our dataset, reflecting both the potential and
challenges it offers for vessel segmentation tasks. We hope this challenging
dataset would significantly contribute to the development of eye disease
diagnosis and early prevention