1,273 research outputs found
Retinal blood vessels extraction using probabilistic modelling
© 2014 Kaba et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.This article has been made available through the Brunel Open Access Publishing Fund.The analysis of retinal blood vessels plays an important role in detecting and treating retinal diseases. In this review, we present an automated method to segment blood vessels of fundus retinal image. The proposed method could be used to support a non-intrusive diagnosis in modern ophthalmology for early detection of retinal diseases, treatment evaluation or clinical study. This study combines the bias correction and an adaptive histogram equalisation to enhance the appearance of the blood vessels. Then the blood vessels are extracted using probabilistic modelling that is optimised by the expectation maximisation algorithm. The method is evaluated on fundus retinal images of STARE and DRIVE datasets. The experimental results are compared with some recently published methods of retinal blood vessels segmentation. The experimental results show that our method achieved the best overall performance and it is comparable to the performance of human experts.The Department of Information Systems, Computing and Mathematics, Brunel University
Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography
Automated segmentation of retinal blood vessels in label-free fundus images
entails a pivotal role in computed aided diagnosis of ophthalmic pathologies,
viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases.
The challenge remains active in medical image analysis research due to varied
distribution of blood vessels, which manifest variations in their dimensions of
physical appearance against a noisy background.
In this paper we formulate the segmentation challenge as a classification
task. Specifically, we employ unsupervised hierarchical feature learning using
ensemble of two level of sparsely trained denoised stacked autoencoder. First
level training with bootstrap samples ensures decoupling and second level
ensemble formed by different network architectures ensures architectural
revision. We show that ensemble training of auto-encoders fosters diversity in
learning dictionary of visual kernels for vessel segmentation. SoftMax
classifier is used for fine tuning each member auto-encoder and multiple
strategies are explored for 2-level fusion of ensemble members. On DRIVE
dataset, we achieve maximum average accuracy of 95.33\% with an impressively
low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 .
Comparison with other major algorithms substantiates the high efficacy of our
model.Comment: Accepted as a conference paper at IEEE EMBC, 201
Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation
Experts use retinal images and vessel trees to detect and diagnose various
eye, blood circulation, and brain-related diseases. However, manual
segmentation of retinal images is a time-consuming process that requires high
expertise and is difficult due to privacy issues. Many methods have been
proposed to segment images, but the need for large retinal image datasets
limits the performance of these methods. Several methods synthesize deep
learning models based on Generative Adversarial Networks (GAN) to generate
limited sample varieties. This paper proposes a novel Denoising Diffusion
Probabilistic Model (DDPM) that outperformed GANs in image synthesis. We
developed a Retinal Trees (ReTree) dataset consisting of retinal images,
corresponding vessel trees, and a segmentation network based on DDPM trained
with images from the ReTree dataset. In the first stage, we develop a two-stage
DDPM that generates vessel trees from random numbers belonging to a standard
normal distribution. Later, the model is guided to generate fundus images from
given vessel trees and random distribution. The proposed dataset has been
evaluated quantitatively and qualitatively. Quantitative evaluation metrics
include Frechet Inception Distance (FID) score, Jaccard similarity coefficient,
Cohen's kappa, Matthew's Correlation Coefficient (MCC), precision, recall,
F1-score, and accuracy. We trained the vessel segmentation model with synthetic
data to validate our dataset's efficiency and tested it on authentic data. Our
developed dataset and source code is available at
https://github.com/AAleka/retree.Comment: International Conference on Computational Photography 2023 (ICCP
2023
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation
Vessel segmentation in medical images is one of the important tasks in the
diagnosis of vascular diseases and therapy planning. Although learning-based
segmentation approaches have been extensively studied, a large amount of
ground-truth labels are required in supervised methods and confusing background
structures make neural networks hard to segment vessels in an unsupervised
manner. To address this, here we introduce a novel diffusion adversarial
representation learning (DARL) model that leverages a denoising diffusion
probabilistic model with adversarial learning, and apply it to vessel
segmentation. In particular, for self-supervised vessel segmentation, DARL
learns the background signal using a diffusion module, which lets a generation
module effectively provide vessel representations. Also, by adversarial
learning based on the proposed switchable spatially-adaptive denormalization,
our model estimates synthetic fake vessel images as well as vessel segmentation
masks, which further makes the model capture vessel-relevant semantic
information. Once the proposed model is trained, the model generates
segmentation masks in a single step and can be applied to general vascular
structure segmentation of coronary angiography and retinal images. Experimental
results on various datasets show that our method significantly outperforms
existing unsupervised and self-supervised vessel segmentation methods.Comment: Accepted at ICLR 202
Extraction of Airways with Probabilistic State-space Models and Bayesian Smoothing
Segmenting tree structures is common in several image processing
applications. In medical image analysis, reliable segmentations of airways,
vessels, neurons and other tree structures can enable important clinical
applications. We present a framework for tracking tree structures comprising of
elongated branches using probabilistic state-space models and Bayesian
smoothing. Unlike most existing methods that proceed with sequential tracking
of branches, we present an exploratory method, that is less sensitive to local
anomalies in the data due to acquisition noise and/or interfering structures.
The evolution of individual branches is modelled using a process model and the
observed data is incorporated into the update step of the Bayesian smoother
using a measurement model that is based on a multi-scale blob detector.
Bayesian smoothing is performed using the RTS (Rauch-Tung-Striebel) smoother,
which provides Gaussian density estimates of branch states at each tracking
step. We select likely branch seed points automatically based on the response
of the blob detection and track from all such seed points using the RTS
smoother. We use covariance of the marginal posterior density estimated for
each branch to discriminate false positive and true positive branches. The
method is evaluated on 3D chest CT scans to track airways. We show that the
presented method results in additional branches compared to a baseline method
based on region growing on probability images.Comment: 10 pages. Pre-print of the paper accepted at Workshop on Graphs in
Biomedical Image Analysis. MICCAI 2017. Quebec Cit
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
Accurate and reliable segmentation of the optic disc in digital fundus images
We describe a complete pipeline for the detection and accurate automatic segmentation of the optic disc in digital fundus images. This procedure provides separation of vascular information and accurate inpainting of vessel-removed images, symmetry-based optic disc localization, and fitting of incrementally complex contour models at increasing resolutions using information related to inpainted images and vessel masks. Validation experiments, performed on a large dataset of images of healthy and pathological eyes, annotated by experts and partially graded with a quality label, demonstrate the good performances of the proposed approach. The method is able to detect the optic disc and trace its contours better than the other systems presented in the literature and tested on the same data. The average error in the obtained contour masks is reasonably close to the interoperator errors and suitable for practical applications. The optic disc segmentation pipeline is currently integrated in a complete software suite for the semiautomatic quantification of retinal vessel properties from fundus camera images (VAMPIRE)
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