Mr. Yufan He Profile

Yufan He

at Johns Hopkins Univ

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Publications (6)

Proceedings Article | 10 March 2020 Paper

Variational intensity cross channel encoder for unsupervised vessel segmentation on OCT angiography

Yihao Liu, Lianrui Zuo, Aaron Carass, Yufan He, Angeliki Filippatou, Sharon Solomon, Shiv Saidha, Peter Calabresi, Jerry Prince

Proceedings Volume 11313, 113130Y (2020) https://doi.org/10.1117/12.2549967

KEYWORDS: Image segmentation, Angiography, Optical coherence tomography, Network architectures, Capillaries, Eye

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Proceedings Article | 10 March 2020 Paper

Segmenting retinal OCT images with inter-B-scan and longitudinal information

Yufan He, Aaron Carass, Yihao Liu, Angeliki Filippatou, Bruno Jedynak, Sharon Solomon, Shiv Saidha, Peter Calabresi, Jerry Prince

Proceedings Volume 11313, 113133C (2020) https://doi.org/10.1117/12.2549857

KEYWORDS: Image segmentation, Optical coherence tomography, 3D image processing, Convolution, Image registration, Network architectures, Retina, Computer programming

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Proceedings Article | 10 March 2020 Presentation + Paper

Adversarial domain adaptation for multi-device retinal OCT segmentation

Yufan He, Aaron Carass, Yihao Liu, Shiv Saidha, Peter Calabresi, Jerry Prince

Proceedings Volume 11313, 1131309 (2020) https://doi.org/10.1117/12.2549839

KEYWORDS: Image segmentation, Data modeling, Optical coherence tomography, Convolution

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Proceedings Article | 10 March 2020 Presentation + Paper

Finding novelty with uncertainty

Jacob Reinhold, Yufan He, Shizhong Han, Yunqiang Chen, Dashan Gao, Junghoon Lee, Jerry Prince, Aaron Carass

Proceedings Volume 11313, 113130H (2020) https://doi.org/10.1117/12.2549341

KEYWORDS: Image segmentation, Computed tomography, Pathology, Data modeling, Medical imaging, Brain, Magnetic resonance imaging, Neural networks, Statistical modeling, Head

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Proceedings Article | 15 March 2019 Presentation + Paper

Cerebellum parcellation with convolutional neural networks

Shuo Han, Yufan He, Aaron Carass, Sarah Ying, Jerry Prince

Proceedings Volume 10949, 109490K (2019) https://doi.org/10.1117/12.2512119

KEYWORDS: Cerebellum, Image segmentation, Convolutional neural networks, Magnetic resonance imaging, Network architectures, Image processing algorithms and systems, Medicine

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Proceedings Article | 2 March 2018 Presentation + Paper

Inter-scanner variation independent descriptors for constrained diffeomorphic demons registration of retina OCT

S. Reaungamornrat, A. Carass, Y. He, S. Saidha, P. Calabresi, J. Prince

Proceedings Volume 10574, 105741B (2018) https://doi.org/10.1117/12.2293790

KEYWORDS: Optical coherence tomography, Image registration, Denoising, Silver, Retina, Image quality, Image resolution, Speckle, Image segmentation, Rigid registration

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Purpose: OCT offers high in-plane micrometer resolution, enabling studies of neurodegenerative and ocular-disease mechanisms via imaging of the retina at low cost. An important component to such studies is inter-scanner deformable image registration. Image quality of OCT, however, is suboptimal with poor signal-to-noise ratio and through-plane resolution. Geometry of OCT is additionally improperly defined. We developed a diffeomorphic deformable registration method incorporating constraints accommodating the improper geometry and a decentralized-modality-insensitiveneighborhood-descriptors (D-MIND) robust against degradation of OCT image quality and inter-scanner variability. Method: The method, called D-MIND Demons, estimates diffeomorphisms using D-MINDs under constraints on the direction of velocity fields in a MIND-Demons framework. Descriptiveness of D-MINDs with/without denoising was ranked against four other shape/texture-based descriptors. Performance of D-MIND Demons and its variants incorporating other descriptors was compared for cross-scanner, intra- and inter-subject deformable registration using clinical retina OCT data. Result: D-MINDs outperformed other descriptors with the difference in mutual descriptiveness between high-contrast and homogenous regions > 0.2. Among Demons variants, D-MIND-Demons was computationally efficient, demonstrating robustness against OCT image degradation (noise, speckle, intensity-non-uniformity, and poor throughplane resolution) and consistent registration accuracy [(4±4 μm) and (4±6 μm) in cross-scanner intra- and inter-subject registration] regardless of denoising. Conclusions: A promising method for cross-scanner, intra- and inter-subject OCT image registration has been developed for ophthalmological and neurological studies of retinal structures. The approach could assist image segmentation, evaluation of longitudinal disease progression, and patient population analysis, which in turn, facilitate diagnosis and patient-specific treatment.

Showing 5 of 6 publications

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