Can we make a more efficient U-Net for blood vessel segmentation?

Liu, Qiong; Zhong, Ziming; Sengupta, Sourya; Lakshminarayanan, Vasudevan

doi:10.1117/12.2567861

Cited by 3 publications

(2 citation statements)

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“…For example, Bamba et al, [198] used a U-net architecture with 3D convolutions that allow the use of an attention gate for the decoder to suppress unimported parts of the input, while emphasizing the relevant features. There is considerable room for improvement and innovation (e.g., [199]).…”

Section: Feature Extractionmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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Medical brain image analysis is a necessary step in computer-assisted/computer-aided diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of white matter hyperintensities (WMHs) of brain magnetic resonance (MR) images, specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used bibliometric networks. Of a total of 140 documents, we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and development of new deep learning models to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with good performance metrics (e.g., Dice similarity coefficient, DSC: 0.99) were found; however, there is little practical application due to the use of small datasets and a lack of reproducibility. Therefore, the main conclusion is that there should be multidisciplinary research groups to overcome the gap between CAD developments and their deployment in the clinical environment.

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Section: Feature Extractionmentioning

confidence: 99%

MR Images, Brain Lesions, and Deep Learning

2021

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“…Bamba et al, [182], used a U-net architecture with 3D convolutions that allow the use of an attention gate for the decoder to suppress unimported parts of the input while emphasize the relevant features. There is considerable room for improvement and innovation of innovative networks (e.g., [183]).…”

Section: Feature Extractionmentioning

confidence: 99%

MRI Images, Brain Lesions and Deep Learning

Castillo¹,

Lakshminarayanan²,

Rodríguez-Álvarez³

2021

Preprint

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Medical brain image analysis is a necessary step in the Computers Assisted /Aided Diagnosis (CAD) systems. Advancements in both hardware and software in the past few years have led to improved segmentation and classification of various diseases. In the present work, we review the published literature on systems and algorithms that allow for classification, identification, and detection of White Matter Hyperintensities (WMHs) of brain MRI images specifically in cases of ischemic stroke and demyelinating diseases. For the selection criteria, we used the bibliometric networks. Out of a total of 140 documents we selected 38 articles that deal with the main objectives of this study. Based on the analysis and discussion of the revised documents, there is constant growth in the research and proposal of new models of deep learning to achieve the highest accuracy and reliability of the segmentation of ischemic and demyelinating lesions. Models with indicators (Dice Score, DSC: 0.99) were found, however with little practical application due to the uses of small datasets and lack of reproducibility. Therefore, the main conclusion is to establish multidisciplinary research groups to overcome the gap between CAD developments and their complete utilization in the clinical environment.

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Weighted skip-connection feature fusion: A method for augmenting UAV oriented rice panicle image segmentation

Xiao

Pan

et al. 2023

Computers and Electronics in Agriculture

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Can we make a more efficient U-Net for blood vessel segmentation?

Cited by 3 publications

References 0 publications

MR Images, Brain Lesions, and Deep Learning

MR Images, Brain Lesions, and Deep Learning

MRI Images, Brain Lesions and Deep Learning

Weighted skip-connection feature fusion: A method for augmenting UAV oriented rice panicle image segmentation

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