Difference between revisions of "Publications/2024"

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(Added "On Finite Difference Jacobian Computation in Deformable Image Registration".)
(Added some web links.)
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{{iacl-pub| author = J. Zhang, L. Zuo, B.E. Dewey, S.W. Remedios, S.P. Hays, D. L. Pham, J.L. Prince, and A. Carass | title = Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation | conf = spie2024 | doi = }}
 
{{iacl-pub| author = J. Zhang, L. Zuo, B.E. Dewey, S.W. Remedios, S.P. Hays, D. L. Pham, J.L. Prince, and A. Carass | title = Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation | conf = spie2024 | doi = }}
 
{{iacl-pub| author = J. Liu, R. Zhang, C.L. Johnson, A. Carass, J.L. Prince, and A. Alshareef | title = Exploratory magnetic resonance elastography synthesis from magnetic resonance and diffusion tensor imaging | conf = spie2024 | doi = }}
 
{{iacl-pub| author = J. Liu, R. Zhang, C.L. Johnson, A. Carass, J.L. Prince, and A. Alshareef | title = Exploratory magnetic resonance elastography synthesis from magnetic resonance and diffusion tensor imaging | conf = spie2024 | doi = }}
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{{iacl-pub| author = Y. Wang, Y. Liu, S. Wei, Y. Xue, L. Zuo, S. W. Remedios, Z. Bian, M. Meggyesye, J. Ahn, R. Lee, M.G. Luciano, J.L. Prince, and A. Carass | title = Deep learning-based segmentation of hydrocephalus brain ventricle from ultrasound | conf = spie2024 | doi = }}
+
{{iacl-pub| author = Y. Wang, Y. Liu, S. Wei, Y. Xue, L. Zuo, S. W. Remedios, Z. Bian, M. Meggyesye, J. Ahn, R. Lee, M.G. Luciano, J.L. Prince, and A. Carass | title = Deep learning-based segmentation of hydrocephalus brain ventricle from ultrasound | conf = spie2024 | doi = 10.1117/12.3007668}}
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{{iacl-pub| author = S.W. Remedios, S. Wei, B.E. Dewey, A. Carass, D.L. Pham, and J.L. Prince | title = Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR Images | conf = spie2024 | doi = }}
+
{{iacl-pub| author = S.W. Remedios, S. Wei, B.E. Dewey, A. Carass, D.L. Pham, and J.L. Prince | title = Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR Images | conf = spie2024 | doi = 10.1117/12.3007304}}
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{{iacl-pub| author = S.P. Hays, L. Zuo, Y. Liu, J. Zhuo, J.L. Prince, and A. Carass | title = Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis | conf = spie2024 | doi = }}
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{{iacl-pub| author = S.P. Hays, L. Zuo, Y. Liu, J. Zhuo, J.L. Prince, and A. Carass | title = Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis | conf = spie2024 | doi = 10.1117/12.3006425 | arXiv = 2402.12288}}
 
{{iacl-pub| author = Z. Bian, A. Alshareef, S. Wei, J. Chen, Y. Liu, Y. Wang, J. Woo, D.L. Pham, J. Zhuo, A. Carass, and J.L. Prince
 
{{iacl-pub| author = Z. Bian, A. Alshareef, S. Wei, J. Chen, Y. Liu, Y. Wang, J. Woo, D.L. Pham, J. Zhuo, A. Carass, and J.L. Prince
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  | title = Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning? | conf = spie2024 | doi = }}
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  | title = Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning? | conf = spie2024 | doi = 10.1117/12.3006906 | arXiv = 2401.17571}}
 
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{{iacl-pub| author = A. Carass, D. Greenman, B.E. Dewey, P.A. Calabresi, J.L. Prince, and D.L. Pham | title = Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI | journal = NeuroImage: Reports | number = 4(1):100195 | when = 2024 | doi = 10.1016/j.ynirp.2024.100195}}
 
{{iacl-pub| author = A. Carass, D. Greenman, B.E. Dewey, P.A. Calabresi, J.L. Prince, and D.L. Pham | title = Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI | journal = NeuroImage: Reports | number = 4(1):100195 | when = 2024 | doi = 10.1016/j.ynirp.2024.100195}}
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{{iacl-pub| author = C. Liu, Z. Zhuo, L. Qu, Y. Jin, T. Hua, J. Xu, G. Tan, Y. Li, Y. Duan, T. Wang, Z. Zhang, Y. Zhang, R. Chen, P. Yu, P. Zhang, Y. Shi, J. Zhang, D. Tian, R. Li, X. Zhang, F.-D. Shi, Y. Wang, J. Jiang, A. Carass, Y. Liu, and C. Ye | title = DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training | journal = Science Bulletin | number = In Press | when = 2024 | doi = 10.1016/j.scib.2024.01.034 }}
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{{iacl-pub| author = C. Liu, Z. Zhuo, L. Qu, Y. Jin, T. Hua, J. Xu, G. Tan, Y. Li, Y. Duan, T. Wang, Z. Zhang, Y. Zhang, R. Chen, P. Yu, P. Zhang, Y. Shi, J. Zhang, D. Tian, R. Li, X. Zhang, F.-D. Shi, Y. Wang, J. Jiang, A. Carass, Y. Liu, and C. Ye | title = DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training | journal = Science Bulletin | number = 69(7):872-875 | when = 2024 | doi = 10.1016/j.scib.2024.01.034 }}
 
{{iacl-pub| author = Y. Liu, L. Zuo, Y. He, S. Han, J. Lei, J.L. Prince, and A. Carass | title = OCTA Segmentation with Limited Training Data Using Disentangled Representation Learning | pub = Deep Learning for Medical Image Analysis (Second Edition), edited by S.K. Zhou, H. Greenspan, and D. Shen | number = Chapter 16, pp. 451–469 | when = 2024 | doi = 10.1016/B978-0-32-385124-4.00027-1}}
 
{{iacl-pub| author = Y. Liu, L. Zuo, Y. He, S. Han, J. Lei, J.L. Prince, and A. Carass | title = OCTA Segmentation with Limited Training Data Using Disentangled Representation Learning | pub = Deep Learning for Medical Image Analysis (Second Edition), edited by S.K. Zhou, H. Greenspan, and D. Shen | number = Chapter 16, pp. 451–469 | when = 2024 | doi = 10.1016/B978-0-32-385124-4.00027-1}}
 
{{iacl-pub| author = L. Zuo, Y. Liu, J. L. Prince, and A. Carass | title = An overview of disentangled representation learning for MR image harmonization | pub = Deep Learning for Medical Image Analysis (Second Edition), edited by S.K. Zhou, H. Greenspan, and D. Shen | number = Chapter 5, pp. 135–152 | when = 2024 | doi = 10.1016/B978-0-32-385124-4.00014-3}}
 
{{iacl-pub| author = L. Zuo, Y. Liu, J. L. Prince, and A. Carass | title = An overview of disentangled representation learning for MR image harmonization | pub = Deep Learning for Medical Image Analysis (Second Edition), edited by S.K. Zhou, H. Greenspan, and D. Shen | number = Chapter 5, pp. 135–152 | when = 2024 | doi = 10.1016/B978-0-32-385124-4.00014-3}}

Revision as of 18:18, 29 April 2024

2024

  • Y. Liu, J. Chen, S. Wei, A. Carass, and J.L. Prince, "On Finite Difference Jacobian Computation in Deformable Image Registration", International Journal of Computer Vision, April 2024. DOI: "On Finite Difference Jacobian Computation in Deformable Image Registration"
  • J. Zhang, L. Zuo, B.E. Dewey, S.W. Remedios, D.L. Pham, A. Carass, and J.L. Prince, "Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion", 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece, May 27–30, 2024.
  • J. Chen, Y. Liu, S. Wei, Z. Bian, A. Carass, and Y. Du, "From registration uncertainty to segmentation uncertainty", 21st IEEE International Symposium on Biomedical Imaging (ISBI 2024), Athens, Greece, May 27–30, 2024.
  • Z. Wu, S.W. Remedios, B.E. Dewey, A. Carass, and J.L. Prince, "AniRes2D: Anisotropic residual-enhanced diffusion for 2D MR super-resolution", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024.
  • J. Zhang, L. Zuo, B.E. Dewey, S.W. Remedios, S.P. Hays, D. L. Pham, J.L. Prince, and A. Carass, "Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024.
  • J. Liu, R. Zhang, C.L. Johnson, A. Carass, J.L. Prince, and A. Alshareef, "Exploratory magnetic resonance elastography synthesis from magnetic resonance and diffusion tensor imaging", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024.
  • Y. Wang, Y. Liu, S. Wei, Y. Xue, L. Zuo, S. W. Remedios, Z. Bian, M. Meggyesye, J. Ahn, R. Lee, M.G. Luciano, J.L. Prince, and A. Carass, "Deep learning-based segmentation of hydrocephalus brain ventricle from ultrasound", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024. DOI: "Deep learning-based segmentation of hydrocephalus brain ventricle from ultrasound"
  • S.W. Remedios, S. Wei, B.E. Dewey, A. Carass, D.L. Pham, and J.L. Prince, "Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR Images", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024. DOI: "Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR Images"
  • S.P. Hays, L. Zuo, Y. Liu, J. Zhuo, J.L. Prince, and A. Carass, "Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024. DOI: "Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis" arXiv: "Revisiting registration-based synthesis: A focus on unsupervised MR image synthesis"
  • Z. Bian, A. Alshareef, S. Wei, J. Chen, Y. Liu, Y. Wang, J. Woo, D.L. Pham, J. Zhuo, A. Carass, and J.L. Prince, "Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?", Proceedings of SPIE Medical Imaging (SPIE-MI 2024), San Diego, CA, February 18–22, 2024. DOI: "Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?" arXiv: "Is Registering Raw Tagged-MR Enough for Strain Estimation in the Era of Deep Learning?"
  • A. Carass, D. Greenman, B.E. Dewey, P.A. Calabresi, J.L. Prince, and D.L. Pham, "Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI", NeuroImage: Reports, 4(1):100195, 2024. DOI: "Image harmonization improves consistency of intra-rater delineations of MS lesions in heterogeneous MRI"
  • C. Liu, Z. Zhuo, L. Qu, Y. Jin, T. Hua, J. Xu, G. Tan, Y. Li, Y. Duan, T. Wang, Z. Zhang, Y. Zhang, R. Chen, P. Yu, P. Zhang, Y. Shi, J. Zhang, D. Tian, R. Li, X. Zhang, F.-D. Shi, Y. Wang, J. Jiang, A. Carass, Y. Liu, and C. Ye, "DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training", Science Bulletin, 69(7):872-875, 2024. DOI: "DeepWMH: A deep learning tool for accurate white matter hyperintensity segmentation without requiring manual annotations for training"
  • Y. Liu, L. Zuo, Y. He, S. Han, J. Lei, J.L. Prince, and A. Carass, "OCTA Segmentation with Limited Training Data Using Disentangled Representation Learning", Deep Learning for Medical Image Analysis (Second Edition), edited by S.K. Zhou, H. Greenspan, and D. Shen, Chapter 16, pp. 451–469, 2024. DOI: "OCTA Segmentation with Limited Training Data Using Disentangled Representation Learning"
  • L. Zuo, Y. Liu, J. L. Prince, and A. Carass, "An overview of disentangled representation learning for MR image harmonization", Deep Learning for Medical Image Analysis (Second Edition), edited by S.K. Zhou, H. Greenspan, and D. Shen, Chapter 5, pp. 135–152, 2024. DOI: "An overview of disentangled representation learning for MR image harmonization"
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