DilUnet: A U-net based architecture for blood vessels segmentation
Introduction
Various health-related complications can occur in the elderly. Diabetic retinopathy, age-related muscular degeneration, and glaucoma are a few of the diseases that are most prevalent in old age. Vessel segmentation of the fundus image can provide sufficient information for the diagnosis and prognosis of such ailments. Different retinal vessel features, such as length, width, thickness, angles, and branches, correspond to a particular health condition [1,2]. Early symptoms of most of these health conditions are linked to changes in the structure of the retinal blood vessels. These vasculatures are important in terms of medical image diagnostics, as they can be observed through a noninvasive procedure [3]. Therefore, by studying the structural changes in the blood vessels of the fundus, ophthalmologists can detect most of these age-related diseases in their early stages for effective treatment and prevention. However, manual labeling of the blood vessels from a fundus image is quite labor-intensive, as it requires an expert with sufficient knowledge in the field of ophthalmology and many hours of work. Moreover, due to human error and subjective judgment, different experts might label blood vessels according to their perception. Therefore, the traditional method of eye screening is inefficient and time-consuming.
Automatic segmentation of the vasculature of the retina is necessary to reduce costs and save time. However, due to the varying shape, size, and nature of vascular circuitry, its complexity and the low contrast of thin vessels against the background of the retina, automatic segmentation of blood vessels is a challenging task that requires extensive research, experience, and knowledge in the field of computer vision and image processing.
Over the last few years, much work has been conducted on vessel segmentation. However, due to the challenges discussed earlier, it remains a daunting task, and there are many unexplored possibilities. Therefore, in this paper, we propose an overall improved, U-net variant that performs better at correctly classifying vessels than most state-of-the-art, vessel segmentation methods. The following points are the main contributions of this work in the field of biomedical image segmentation:
- 1.
An improved U-net architecture that effectively utilizes dilated convolutions as the main form of convolutions that serve as the building blocks along with multi-input modules and multioutput modules to ensure better feature transfer and accurate classification, respectively. Our proposed method of blood vessel segmentation performed better than most of the other existing methods.
- 2.
We proposed weighted multioutput fusion, where the feature map from each decoder path is upsampled to image size resolution and then weighted and fused. This weighted output is then passed through a final layer of a 1 × 1 transpose convolution to obtain the final output. The weighted fusion allows us to extract mostly meaningful features from each classifier and to accurately identify the thin blood vessels, which greatly improves the overall sensitivity.
- 3.
The decoder, encoder blocks, and skip paths are comprised of dilated convolutions of different rates that are uniquely combined to capture blood vessels of different shapes and sizes, which yields overall better performance.
- 4.
Our method achieved the highest sensitivity against many SOTA blood vessel segmentation works and yields fairly competitive results under noisy data conditions, ensuring robustness.
The remainder of the paper is organized as follows: Section 2 discusses certain previous related works Section 3 provides a detailed description of the proposed methodology that is adopted and the architecture of the network Section 4 includes the test results, compares performances, and discusses differences among the related works, including an ablation study of the architecture Section 5 concludes this work.
Section snippets
Related works
The task of automated retinal image segmentation can be divided into two categories namely: supervised learning methods and unsupervised learning methods. Unsupervised methods include rule-based approaches that may utilize a matched filter and morphological operations to detect and segment the vessels. Zardadi et al. [4] applied the combination of adaptive thresholding and different morphological operations to segment blood vessels. In their work, first, images were enhanced in preprocessing;
Materials and methods
The original retinal images are RGB-colored images that are passed through a preprocessing pipeline to obtain the final grayscale, resized image for training. These images are fed to our proposed segmentation network for training, and the results are obtained. The process heavily utilizes image augmentation [33] for effective training and to overcome the problems of data limitation and overfitting. The model outputs the final prediction while taking into account the features from all the
Training
This model utilizes image augmentation to work with a limited amount of data [33]. Aside from the use of traditional data augmentation of horizontal/vertical flips and random rotations, we augmented our data with random addition of Gaussian noise, as well as elastic transformation and grid distortion. These operations of elastic transformation, flips, random rotations, and grid distortion are applied to both the image and the labels. This approach makes our model robust to vessels with
Conclusion
In this work, we investigated the U-net architecture for image segmentation and determined that there is still room for improvement. We designed a new set of modules that can be introduced into the already existing U-net for even better performance than the original U-net and most of its other variants. In summary, the main contributions of our work are presented as follows:
- •
The added multioutput block with momentum-based output fusion, multiscale input, optimized implementation of dilated
Acknowledgments
The authors thank the editors and anonymous reviewers for their useful suggestions. This study was supported by the Teaching & Learning Reform of Graduate Education of Central South University, National Natural Science Foundation of China (Grant no. 61502537), Hunan Provincial Natural Science Foundation of China, and Fundamental Research Funds for the Central Universities of Central South University.
References (61)
- et al.
“Detection of glaucomatous change based on vessel shape analysis”
Comput. Med. Imaging Graph.
(2008) - et al.
“Retinal blood vessel segmentation from diabetic retinopathy images using tandem PCNN model and deep learning based SVM”
Optik
(2019) - et al.
“Retinal vasculature: structure and pathologies”
Pathobiology of Human Disease: A Dynamic Encyclopedia of Disease Mechanisms
(2014) - et al.
“Blood vessel detection from retinal fundas images using GIFKCN classifier”
Procedia Comput. Sci.
(2020) “An efficient retinal blood vessel segmentation in eye fundus images by using optimized top-hat and homomorphic filtering”
Comput. Methods Programs Biomed.
(2021)“Retinal vessel segmentation in colour fundus images using extreme learning machine”
Comput. Med. Imaging Graph.
(2017)- et al.
“Retinal vessel segmentation using dense U-net with multiscale inputs”
J. Med. Imaging
(2019) - et al.
“MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation”
Neural Netw.
(2020) "Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function"
Neurocomputing
(2018)"Blood vessel segmentation from fundus image by a cascade classification framework"
Pattern Recognit.
(2019)
“Color, Shading, and Lighting”, in Advanced Graphics Programming Using OpenGL
“Adaptive histogram equalization and its variations"
Comput. Vision Graph. Image Process.
Chapter 6 - Selection of Variables and Factor Derivation,” in Commercial Data M
“Segmentation of retinal blood vessels using the radial projection and semi-supervised approach”
Pattern Recognit.
“Trainable COSFIRE filters for vessel delineation with application to retinal images”
Med. Image Anal.
“Retinal vessel delineation using a brain-inspired wavelet transform and random forest”
Pattern Recognit.
“Retinal vessel segmentation based on fully convolutional neural networks”
Expert Syst. Appl.
“DUNet: a deformable network for retinal vessel segmentation”
Knowl. Based Syst.
“Multi-proportion channel ensemble model for retinal vessel segmentation”
Comput. Biol. Med.
“BTS-DSN: deeply supervised neural network with short connections for retinal vessel segmentation”
Int. J. Med. Inform.
“NFN+: a novel network followed network for retinal vessel segmentation”
Neural Netw.
“Unsupervised segmentation of retinal blood vessels using the human visual system line detection model”
J. Inf. Syst. Telecommun.
“A new robust method for blood vessel segmentation in retinal fundus images based on weighted line detector and hidden Markov model”
Comput. Methods Programs Biomed.
“Ridge-based vessel segmentation in color images of the retina”
IEEE Trans. Med. Imaging
“An ensemble classification-based approach applied to retinal blood vessel segmentation”
IEEE Trans. Biomed. Eng.
“Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response”
IEEE Trans. Med. Imaging
“Retinal blood vessel segmentation using an extreme learning machine approach”
“FABC: retinal vessel segmentation using AdaBoost”
IEEE Trans. Inf. Technol. Biomed.
“Automatic retinal blood vessel segmentation based on fully convolutional neural networks”
Symmetry
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