Cerebrovascular Network Segmentation of MRA Images With Deep Learning

Sanchesa, Pedro; Meyer, Cyril; Vigon, Vincent; Naegel, Benoît

doi:10.1109/isbi.2019.8759569

Cited by 54 publications

(45 citation statements)

References 12 publications

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“…Recently, deep learning methods have been widely used in medical image segmentation (36), among which UNet is the most commonly used for vessel segmentation (15). UNet has been extensively used in retinal vessel segmentation (37)(38)(39)(40), 3D cerebrovascular segmentation (41)(42)(43), and cardiac vessel segmentation. Sevastopolsky et al (44) applied UNet to segment the optic disc and cup in retinal fundus images to diagnose glaucoma.…”

Section: Discussionmentioning

confidence: 99%

Automated Quantitative Analysis of Blood Flow in Extracranial–Intracranial Arterial Bypass Based on Indocyanine Green Angiography

Jiang

Zhang

et al. 2021

Front. Surg.

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Microvascular imaging based on indocyanine green is an important tool for surgeons who carry out extracranial–intracranial arterial bypass surgery. In terms of blood perfusion, indocyanine green images contain abundant information, which cannot be effectively interpreted by humans or currently available commercial software. In this paper, an automatic processing framework for perfusion assessments based on indocyanine green videos is proposed and consists of three stages, namely, vessel segmentation based on the UNet deep neural network, preoperative and postoperative image registrations based on scale-invariant transform features, and blood flow evaluation based on the Horn–Schunck optical flow method. This automatic processing flow can reveal the blood flow direction and intensity curve of any vessel, as well as the blood perfusion changes before and after an operation. Commercial software embedded in a microscope is used as a reference to evaluate the effectiveness of the algorithm in this study. A total of 120 patients from multiple centers were sampled for the study. For blood vessel segmentation, a Dice coefficient of 0.80 and a Jaccard coefficient of 0.73 were obtained. For image registration, the success rate was 81%. In preoperative and postoperative video processing, the coincidence rates between the automatic processing method and commercial software were 89 and 87%, respectively. The proposed framework not only achieves blood perfusion analysis similar to that of commercial software but also automatically detects and matches blood vessels before and after an operation, thus quantifying the flow direction and enabling surgeons to intuitively evaluate the perfusion changes caused by bypass surgery.

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Section: Discussionmentioning

confidence: 99%

Automated Quantitative Analysis of Blood Flow in Extracranial–Intracranial Arterial Bypass Based on Indocyanine Green Angiography

Jiang

Zhang

et al. 2021

Front. Surg.

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“…on retinal imaging [53]. However, very recent works also aim at considering more complex cases, in 3D (bio)medical imaging modalities [54], [55], [56]. In this context, our mixed gradient may be involved as an additional loss term for strengthening connectivity of thin, curvilinear structures in complex nD images.…”

Section: Discussionmentioning

confidence: 99%

$n$ D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization

Merveille

Naegel

Talbot

et al. 2019

IEEE Trans. on Image Process.

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Curvilinear structure restoration in image processing procedures is a difficult task, which can be compounded when these structures are thin, i.e. when their smallest dimension is close to the resolution of the sensor. Many recent restoration methods involve considering a local gradient-based regularization term as prior, assuming gradient sparsity. An isotropic gradient operator is typically not suitable for thin curvilinear structures, since gradients are not sparse for these. In this article, we propose a mixed gradient operator that combines a standard gradient in the isotropic image regions, and a directional gradient in the regions where specific orientations are likely. In particular, such information can be provided by curvilinear structure detectors (e.g. RORPO or Frangi filters). Our proposed mixed gradient operator, that can be viewed as a companion tool of such detectors, is proposed in a discrete framework and its formulation / computation holds in any dimension; in other words, it is valid in Z n , n ≥ 1. We show how this mixed gradient can be used to construct image priors that take edge orientation as well as intensity into account, and then involved in various image processing tasks while preserving curvilinear structures. Experiments carried out on 2D, 3D, real and synthetic images illustrate the relevance of the proposed gradient and its use in variational frameworks for both denoising and segmentation tasks.

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“…Major contributions of some of the researchers who aimed at developing a system for cerebrovascular segmentation are summarized below. Sanches et al [1] proposed a cerebrovascular segmentation method using deep learning. A 3D model called Uception which is inspired from U-Net architecture is discussed in this paper.…”

Section: Related Workmentioning

confidence: 99%

“…Magnetic resonance angiography (MRA) is the one of the common imaging techniques used to perform this function, which consists in a magnetic resonance imaging (MRI) that looks specifically the blood flow in the brain vessels when measuring. Different methods of MRA are time-offlight (TOF), phase contrast (PC), and fresh blood imaging (FBI) and contrast-enhanced MRA [1]. TOF MRA is the most commonly used imaging modalities in non-invasive vascular research [2].…”

Section: Introductionmentioning

confidence: 99%

Automatic cerebrovascular segmentation methods-a review

Taher

Prakash

2021

IJ-AI

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Cerebrovascular diseases are one of the serious causes for the increase in mortality rate in the world which affect the blood vessels and blood supply to the brain. In order, diagnose and study the abnormalities in the cerebrovascular system, accurate segmentation methods can be used. The shape, direction and distribution of blood vessels can be studied using automatic segmentation. This will help the doctors to envisage the cerebrovascular system. Due to the complex shape and topology, automatic segmentation is still a challenge to the clinicians. In this paper, some of the latest approaches used for segmentation of magnetic resonance angiography images are explained. Some of such methods are deep convolutional neural network (CNN), 3dimentional-CNN (3D-CNN) and 3D U-Net. Finally, these methods are compared for evaluating their performance. 3D U-Net is the better performer among the described methods.

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Cerebrovascular Network Segmentation of MRA Images With Deep Learning

Cited by 54 publications

References 12 publications

Automated Quantitative Analysis of Blood Flow in Extracranial–Intracranial Arterial Bypass Based on Indocyanine Green Angiography

Automated Quantitative Analysis of Blood Flow in Extracranial–Intracranial Arterial Bypass Based on Indocyanine Green Angiography

$n$ D Variational Restoration of Curvilinear Structures With Prior-Based Directional Regularization

Automatic cerebrovascular segmentation methods-a review

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