Fast and efficient retinal blood vessel segmentation method based on deep learning network

Boudegga, Henda; Elloumi, Yaroub; Akil, Mohamed; Bedoui, Sammy; Kachouri, Rostom; Abdallah, Asma Ben

doi:10.1016/j.compmedimag.2021.101902

Cited by 65 publications

(43 citation statements)

References 67 publications

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“…In order to assess the validity of the herein presented approach, we compared our method to the state-of-theart methods in retina segmentation, since, to the best of our knowledge, our approach is one of the first attempts to segment vessels in the ilio-femoral district on 2-D projective images. Boudegga et al [28] proposed a new architecture untitled ''RV-Net'' for retinal vessel tree segmentation, achieving an average accuracy of 0.978 and 0.98, respectively, for DRIVE and STARE database fundus images. Atli et al [29] presented Sine-Net, a novel approach for retinal vessel segmentation.…”

Section: Resultsmentioning

confidence: 99%

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Bruno

Spadea

Scaramuzzino

et al. 2022

Neural Comput & Applic

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The assessment of vascular complexity in the lower limbs provides relevant information about peripheral artery occlusive diseases (PAOD), thus fostering improvements both in therapeutic decisions and prognostic estimation. The current clinical practice consists of visually inspecting and evaluating cine-angiograms of the interested region, which is largely operator-dependent. We present here an automatic method for segmenting the vessel tree and compute a quantitative measure, in terms of fractal dimension (FD), of the vascular complexity. The proposed workflow consists of three main steps: (i) conversion of the cine-angiographies to single static images with a broader field of view, (ii) automatic segmentation of the vascular trees, and (iii) calculation and assessment of FD as complexity index. In particular, this work defines (1) a method to reduce the inter-observer variability in judging vascular complexity in cine-angiography images from patients affected by peripheral artery occlusive disease (PAOD), and (2) the use of Fractal Dimension as a metric of shape complexity of vascular tree. The inter-class correlation coefficient (ICC) is computed as inter-observer agreement metric and to account for possible systematic error, that depends on the experience of the raters. The automatic segmentation of vascular tree achieved an Area Under the Curve mean value of $$0.77~\pm ~0.07$$ 0.77 ± 0.07 , with a min-max range of $$0.57-0.87$$ 0.57 - 0.87 . Absolute operator agreement was higher over the segmented image ($$ICC=0.96$$ I C C = 0.96 ) compared to the video ($$ICC=0.76$$ I C C = 0.76 ) and the a broader field of view image ($$ICC=0.92$$ I C C = 0.92 ). Fractal Dimension computed on both manual segmented images (ground truths) and automatically showed a good correlation with the clinical score (0.85 and 0.75, respectively). Experimental analyses suggest that extracting the vascular tree from cine-angiography can substantially improve the reliability of visual assessment of vascular complexity in PAOD. Results also reveal the effectiveness of FD in evaluating complex vascular tree structures.

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Section: Resultsmentioning

confidence: 99%

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Bruno

Spadea

Scaramuzzino

et al. 2022

Neural Comput & Applic

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“…36 The use of (3Â3) depthwise separable convolutions and the resolution modification through layers imply decreasing the computational complexity with respect to the standard convolutions despite a slight accuracy reduction. 35,37…”

Section: Mobilenet-v2mentioning

confidence: 99%

“…The MobileNet‐V2 blocks are built with residual connections that assist in converging the network weights 36 . The use of (3×3) depthwise separable convolutions and the resolution modification through layers imply decreasing the computational complexity with respect to the standard convolutions despite a slight accuracy reduction 35,37 …”

Section: Ensemble Learning Framework For Cataract Severity Gradingmentioning

confidence: 99%

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

Elloumi

2022

Int J Imaging Syst Tech

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The cataract is the most common cause of severe vision impairment or blindness worldwide. A periodical diagnosis is recommended in order to prevent cataract severity, where screening might be feasibly ensured through fundus images. In this paper, we propose a CAD system to efficiently grade the cataract from fundus images. For such a need, an ensemble learning framework is put forward where the knowledge of three convolutional deep neural networks is stacked in order to perform an efficient cataract prediction. The main contributions of this work are given as follows: (1) Preprocessing and data augmentation of fundus images are carried out to ensure the robustness of the cataract grading; (2) The well‐known DL architectures (Inception‐V3, MobileNet‐V2 and NasNet‐Mobile) are fine‐tuned and learned as base classifiers; (3) An Extreme Learning Machine (ELM) is performed as a meta classifier to stack the predictions of base classifiers. The evaluation is conducted using a dataset of 590 fundus images selected from two public databases. The suggested framework achieves 94.07% accuracy, 94.37% sensitivity, 93.62% specificity, 95.71% precision and 95.04% F‐measure for cataract grading. The experimentation proves that the proposed framework successfully grades fundus images into cataract severity. Moreover, stacking ensemble learning allows achieving a performance that significantly surpasses the ones realized by each DL architecture, applied separately.

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“…Sine-Net novel model is proposed in Atli and Gedik, 12 which comprises residuals to bring further contextual information to the deeper layers. 13 aim to optimize segmentation performance and computation requirements by including lightweight convolution modules. The study of Dharmawan 14 synthesizes various literature methods for retinal blood vessel segmentation to have a fair assessment and comparison.…”

Section: Related Workmentioning

confidence: 99%

“…proposed to use a novel attention gate model that learns automatically to concentrate on target structures of various shapes and sizes. Sine‐Net novel model is proposed in Atli and Gedik, 12 which comprises residuals to bring further contextual information to the deeper layers 13 . aim to optimize segmentation performance and computation requirements by including lightweight convolution modules.…”

Section: Related Workmentioning

confidence: 99%

Modified U‐Net for cytological medical image segmentation

Benazzouz

Benomar

Moualek

2022

Int J Imaging Syst Tech

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Deep learning–based medical image segmentation is henceforth widely established as a powerful segmentation process. This article proposes a new U‐Net architecture based on a convolutional neural network for cytology image segmentation. This structure is more suitable to take into account pixel neighborhood in deconvolution. The goal is to develop an accurate segmentation method for white blood cells segmentation based on cells types features. This new proposed method yields a significant improvement compared to our previous work on the cytological medical dataset. In addition, the performance of the new architecture was also successfully tested on the Digital Retinal Image for Vessel Extraction databases benchmark. The images of this challenge are similar to our cytology image segmentation. Our approach achieved 25% relative improvement of the accuracy compared to the state‐of‐the‐art.

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Fast and efficient retinal blood vessel segmentation method based on deep learning network

Cited by 65 publications

References 67 publications

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Assessing vascular complexity of PAOD patients by deep learning-based segmentation and fractal dimension

Cataract grading method based on deep convolutional neural networks and stacking ensemble learning

Modified U‐Net for cytological medical image segmentation

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