Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter
To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network. We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi...
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| Vydáno v: | Magnetic resonance in medicine Ročník 89; číslo 6; s. 2419 - 2431 |
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01.06.2023
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| ISSN: | 0740-3194, 1522-2594, 1522-2594 |
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| Abstract | To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network.
We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data.
The proposed method increases the true positive rate compared to the rule-based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model.
Combing the filter-based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter-based image processing algorithm, the data driven PVS segmentation model using quality-controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate. |
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| AbstractList | To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network.
We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data.
The proposed method increases the true positive rate compared to the rule-based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model.
Combing the filter-based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter-based image processing algorithm, the data driven PVS segmentation model using quality-controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate. To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network.PURPOSETo develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional neural network.We present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data.METHODSWe present a weakly supervised learning method for PVS segmentation by combing a rule-based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data.The proposed method increases the true positive rate compared to the rule-based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model.RESULTSThe proposed method increases the true positive rate compared to the rule-based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model.Combing the filter-based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter-based image processing algorithm, the data driven PVS segmentation model using quality-controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate.CONCLUSIONSCombing the filter-based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter-based image processing algorithm, the data driven PVS segmentation model using quality-controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate. Click here for author‐reader discussions PurposeTo develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter‐based image processing algorithm and the convolutional neural network.MethodsWe present a weakly supervised learning method for PVS segmentation by combing a rule‐based image processing approach Frangi filter with a canonical deep learning algorithm Unet using conditional random field theory. The weighted cross entropy loss function and the training patch selection were implemented for the optimization and to alleviate the class imbalance issue. The performance of the model was evaluated on the Human Connectome Project data.ResultsThe proposed method increases the true positive rate compared to the rule‐based method and reduces the false positive rate by 36% in the weakly supervised training experiment and 39.4% in the supervised training experiment compared to Unet, which results in superior overall performance. In addition, by training the model on manually quality controlled and annotated data which includes the subjects with the presence of white matter hyperintensities, the proposed method differentiates between PVS and white matter hyperintensities, which reduces the false positive rate by 78.5% compared to weakly supervised trained model.ConclusionsCombing the filter‐based image processing algorithm and the convolutional neural network algorithm could improve the model's segmentation accuracy, while reducing the training dependence on the large scale annotated PVS mask data by the trained physician. Compared to the filter‐based image processing algorithm, the data driven PVS segmentation model using quality‐controlled data as the training target could differentiate the white matter hyperintensity from PVS resulting low false positive rate. |
| Author | Sepehrband, Farshid Custer, Rachel Choupan, Jeiran Sherlock, Patrick Shih, Nien‐Chu Lan, Haoyu Lynch, Kirsten M. Toga, Arthur W. |
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| Cites_doi | 10.1016/j.neuroimage.2012.02.018 10.1016/j.media.2020.101692 10.1016/j.neuroimage.2011.09.015 10.1002/ana.24647 10.3389/neuro.11.022.2009 10.1016/j.neuroimage.2018.08.050 10.1109/TPAMI.2017.2699184 10.1016/j.neuroimage.2013.05.041 10.1016/j.neuroimage.2006.01.015 10.1007/978-3-319-24574-4_28 10.1038/s41598-019-48910-x 10.1016/j.media.2018.02.009 10.1016/j.neuroimage.2022.119329 10.1016/j.neuroimage.2012.01.021 10.1007/BFb0056195 10.1109/TBME.2016.2638918 10.1007/3-540-46805-6_19 10.1016/j.neuroimage.2013.04.127 10.1016/j.neuroimage.2018.10.009 10.1002/mrm.28819 10.1016/j.neuroimage.2018.09.060 10.1038/s41598-018-19781-5 10.1038/s41582-020-0312-z 10.1007/s11263-019-01228-7 |
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| Keywords | conditional random field image processing perivascular spaces structural MRI convolutional neural network |
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for author‐reader discussions To develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter-based image processing algorithm and the convolutional... PurposeTo develop a weakly supervised 3D perivascular spaces (PVS) segmentation model that combines the filter‐based image processing algorithm and the... |
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| SubjectTerms | Algorithms Artificial neural networks Conditional random fields Deep learning Entropy (Information theory) Field theory Humans Image filters Image processing Image Processing, Computer-Assisted - methods Image segmentation Machine learning Magnetic Resonance Imaging - methods Model accuracy Neural networks Neural Networks, Computer Optimization Substantia alba Supervised learning Training |
| Title | Weakly supervised perivascular spaces segmentation with salient guidance of Frangi filter |
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