Fully Automatic White Matter Hyperintensity Segmentation using U-net and Skip Connection

Zhang, Yue; Wu, Jiong; Chen, Wanli; Liu, Yilong; Lyu, Junyan; Shi, Hongcan; Chen, Yifan; Wu, Ed X.; Tang, Xiaoying

doi:10.1109/embc.2019.8856913

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…Brain tumor [106], [107], [110]- [112], [114]- [118] Base U-net [18], [109], [120] 3D U-net [81] Adversarial net; GAN [59], [108] Residual block [113] Dense block [87] Cascaded U-net [92] Residual block; Parallel U-net [44] Inception block; Up skip connections [45] Dense block; Inception block [119] 3D U-net; Residual block [19] 3D U-net, Inception block, Residual block Brain tissue [103], [121]- [124] Base U-net [28], [160] 3D U-net [161] 2.5D U-net [54] Residual block [101] Parallel U-net [41] Attention gate; Residual block White matter tracts [126], [127] U-net with modified skip connections [125] Base U-net [89] Cascaded U-net Fetal brain [128]- [130] Base U-net [131] Base U-net; 3D U-net Stroke lesion/thrombus [133]- [136] Base U-net [132] 3D U-net [69] Dense block; Inception block Cardiovascular structures [138], [140]- [142], [144], …”

Section: Reference Model/methods Usedmentioning

confidence: 99%

“…Various U-net models have been applied on MR images for brain tumor diagnosis [18], [19], [59], [45], [81], [106], [107], [87], [108]- [111], [92], [112]- [119], [44], [120]. U-net has also been applied on brain tissue for investigation of neurological conditions [54], [101], [103], [121]- [124], analysis of white matter tissue [125]- [127], [89], fetal brain development [128]- [131], and stroke lesions [69], [132]- [136].…”

Section: Magnetic Resonance Imaging (Mri)mentioning

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: Reference Model/methods Usedmentioning

confidence: 99%

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

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

et al. 2021

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“…The U-net architectures used in this work are based on the networks first presented in [29]. Starting with the original U-net a few variants, based on work done by [34] and [35], were also tested to find the best performance on our dataset. Considering that U-nets are, at their core, nothing more than a Fully Convolutional Network (FCN) [36] with long distance skip connections, we tested a number of different configurations to find the best performing model for out task.…”

Section: U-net and Variantsmentioning

confidence: 99%

Automatic Pollen Classification and Segmentation Using U-Nets and Synthetic Data

et al. 2022

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Pollen allergies have become one of the most wide-spread afflictions that impact quality of life. This has made the need for automatic pollen detection, classification and monitoring a very important topic. This paper introduces a new public annotated image data-set of pollen with almost 45 thousand samples obtained from an automatic instrument. In this work we apply some of the best performing convolutional neural networks architectures on the task of pollen classification as well as some fully convolutional networks optimized for image segmentation on complex microscope images. We obtain an F1 scores of 0.95 on the new data-set when the best trained model is used as a fully convolutional classifier and a class mean Intersection over Union (IoU) of 0.88 when used as an object detector.

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“…It works differently from existing networks in that V-Net uses convolution layers with strides to downsample and upsample the feature maps rather than pooling. Other networks that deal with 3D data include SCU-Net [7] and MVU-Net [8], which are based on U-Net [9], both producing great accuracy. The nature of the problem and dataset and the MRI properties determine the optimal brain extraction method.…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Framework for Skull Stripping in Brain MRI

Tabassum,

Al Suman,

Russo

et al. 2023

2023 45th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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Skull-stripping, an important pre-processing step in neuroimage computing, involves the automated removal of non-brain anatomy (such as the skull, eyes, and ears) from brain images to facilitate brain segmentation and analysis. Manual segmentation is still practiced, but it is time-consuming and highly dependent on the expertise of clinicians or image analysts. Prior studies have developed various skull-stripping algorithms that perform well on brains with mild or no structural abnormalities. Nonetheless, they were not able to address the issue for brains with significant morphological changes, such as those caused by brain tumors, particularly when the tumors are located near the skull's border. In such cases, a portion of the normal brain may be stripped, or the reverse may occur during skull stripping. To address this limitation, we propose to use a novel deep learning framework based on nnUNet for skull stripping in brain MRI. Two publicly available datasets were used to evaluate the proposed method, including a normal brain MRI dataset -The Neurofeedback Skull-stripped Repository (NFBS), and a brain tumor MRI dataset -The Cancer Genome Atlas (TCGA). The method proposed in the study performed better than six other current methods, namely BSE, ROBEX, UNet, SC-UNet, MV-UNet, and 3D U-Net. The proposed method achieved an average Dice coefficient of 0.9960, a sensitivity of 0.9999, and a specificity of 0.9996 on the NFBS dataset, and an average Dice coefficient of 0.9296, a sensitivity of 0.9288, a specificity of 0.9866 and an accuracy of 0.9762 on the TCGA brain tumor dataset.

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Fully Automatic White Matter Hyperintensity Segmentation using U-net and Skip Connection

Cited by 5 publications

References 14 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

Automatic Pollen Classification and Segmentation Using U-Nets and Synthetic Data

A Deep Learning Framework for Skull Stripping in Brain MRI

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