Multiscale dense convolutional neural network for DSA cerebrovascular segmentation

Cai, Meng; Sun, Kai; Guan, Shaopeng; Wang, Qi; Zong, Rui; Liu, Lei

doi:10.1016/j.neucom.2019.10.035

Cited by 45 publications

(22 citation statements)

References 19 publications

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“…This attribute of dense U-nets is highly desired in medical image analysis due to objects in such images being highly close together, often to the point of overlapping. Applications of dense U-net have been found in analysis of brain tumors [20], [45], retinal blood vessel segmentation [45], cerebral blood vessel segmentation [68], [69], melanoma [70], lung cancer [70], liver cancer [71], and multi-organ segmentation [72].…”

Section: G Dense U-netmentioning

confidence: 99%

“…U-net has been used on OCT for segmentation of retinal layers [339]- [341], blood vessels [342], fluid regions [343], [344], and Drusen [345]. Other uncommon applications are segmentation of blood vessels in digital subtraction angiography (DSA) [68], [346], [347], white matter tract segmentation in diffusion tensor imaging (DTI) [30], iris segmentation in iris imaging [37], tumor detection in mammograms [56], and capillary segmentation in nailfold capillaroscopy [348]. Table 8 collects the applications of U-net based models on some uncommon image modalities.…”

Section: H Other Modalitiesmentioning

confidence: 99%

“…U-net has been used on OCT for segmentation of retinal layers [241], blood vessels [242], fluid regions [243], and Drusen [244]. Other uncommon applications are segmentation of blood vessels in digital subtraction angiography (DSA) [68], [245], white matter tract segmentation in diffusion tensor imaging [30], iris segmentation in iris imaging [37], and tumor detection in mammograms [56].…”

Section: Other Modalitiesmentioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to Xrays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net's potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

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Section: G Dense U-netmentioning

confidence: 99%

Section: H Other Modalitiesmentioning

confidence: 99%

Section: Other Modalitiesmentioning

confidence: 99%

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U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

et al. 2021

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“…Its encoder-decoder structure has been the basis of many new architectures. In the context of vascular tree segmentation it was used, for instance, in [6] for the segmentation of TOF in patients with cerebrovascular disease, or in [7] for the segmentation of digital subtraction angiography. However, all these approaches require a large amount of annotated data to train the networks.…”

Section: A Motivation and Contextmentioning

confidence: 99%

Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures

Sara

Lauzeral

Nouri

et al. 2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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The earlier studies on brain vasculature semantic segmentation used classical image analysis methods to extract the vascular tree from images. Nowadays, deep learning methods are widely exploited for various image analysis tasks. One of the strong restrictions when dealing with neural networks in the framework of semantic segmentation is the need to dispose of a ground truth segmentation dataset, on which the task will be learned. It may be cumbersome to manually segment the arteries in a 3D volumes (MRA-TOF typically). In this work, we aim to tackle the vascular tree segmentation from a new perspective. Our objective is to build an image dataset from mouse vasculatures acquired using CT-Scans, and enhance these vasculatures in such a way to precisely mimic the statistical properties of the human brain. The segmentation of mouse images is easily automatized thanks to their specific acquisition modality. Thus, such a framework allows to generate the data necessary for the training of a Convolutional Neural Networki.e. the enhanced mouse images and there corresponding ground truth segmentation -without requiring any manual segmentation procedure. However, in order to generate an image dataset having consistent properties (strong resemblance with MRA images), we have to ensure that the statistical properties of the enhanced mouse images do match correctly the human MRA acquisitions. In this work, we evaluate at length the similarities between the human arteries as acquired on MRA-TOF and the "humanized" mouse arteries produced by our model. Finally, once the model duly validated, we experiment its applicability with a Convolutional Neural Network.

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“…Zhang et al [ 31 ] proposed a compact convolutional neural network augmented with multiscale feature extraction to carry out diagnosis tasks with limited training samples and presented three cases to verify the effectiveness of the proposed method. Meng et al [ 32 ] proposed a CNN-based framework for digital subtraction angiography cerebrovascular segmentation and obtained some results. Jun et al [ 33 ] proposed a multiscale CNN model for bearings’ remaining useful life predictions, in which the last convolutional layer and pooling layer were combined to form a mixed layer before being connected to the fully connected layer.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

Luo

Lin

Peng³

et al. 2020

Sensors

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Axle-box bearings are one of the most critical mechanical components of the high-speed train. Vibration signals collected from axle-box bearings are usually nonlinear and nonstationary, caused by the complicated operating conditions. Due to the high reliability and real-time requirement of axle-box bearing fault diagnosis for high-speed trains, the accuracy and efficiency of the bearing fault diagnosis method based on deep learning needs to be enhanced. To identify the axle-box bearing fault accurately and quickly, a novel approach is proposed in this paper using a simplified shallow information fusion-convolutional neural network (SSIF-CNN). Firstly, the time domain and frequency domain features were extracted from the training samples and testing samples before been inputted into the SSIF-CNN model. Secondly, the feature maps obtained from each hidden layer were transformed into a corresponding feature sequence by the global convolution operation. Finally, those feature sequences obtained from different layers were concatenated into one-dimensional as the fully connected layer to achieve the fault identification task. The experimental results showed that the SSIF-CNN effectively compressed the training time and improved the fault diagnosis accuracy compared with a general CNN.

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Multiscale dense convolutional neural network for DSA cerebrovascular segmentation

Cited by 45 publications

References 19 publications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

Learning From Mouse CT-Scan Brain Images To Detect MRA-TOF Human Vasculatures

Fault Diagnosis for High-Speed Train Axle-Box Bearing Using Simplified Shallow Information Fusion Convolutional Neural Network

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