Mr. Samuel W. Remedios Profile

Samuel W. Remedios

Doctoral Student at Johns Hopkins Univ

SPIE Involvement:

Author

Publications (19)

SPIE Journal Paper | 14 November 2024 Open Access

Super-resolution multi-contrast unbiased eye atlases with deep probabilistic refinement

Ho Hin Lee, Adam Saunders, Michael Kim, Samuel Remedios, Lucas Remedios, Yucheng Tang, Qi Yang, Xin Yu, Shunxing Bao, Chloe Cho, Louise Mawn, Tonia Rex, Kevin Schey, Blake Dewey, Jeffery Spraggins, Jerry Prince, Yuankai Huo, Bennett Landman

JMI, Vol. 11, Issue 06, 064004, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.064004

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SPIE Journal Paper | 5 November 2024 Open Access

Data-driven nucleus subclassification on colon hematoxylin and eosin using style-transferred digital pathology

Lucas Remedios, Shunxing Bao, Samuel Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Ruining Deng, Nancy Newlin, Adam Saunders, Can Cui, Jia Li, Qi Liu, Ken S. Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman

JMI, Vol. 11, Issue 06, 067501, (November 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.6.067501

KEYWORDS: Education and training, Tissues, Colon, Data modeling, Diseases and disorders, Virtual colonoscopy, Image segmentation, Performance modeling, Pathology, Cross validation

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PurposeCells are building blocks for human physiology; consequently, understanding the way cells communicate, co-locate, and interrelate is essential to furthering our understanding of how the body functions in both health and disease. Hematoxylin and eosin (H&E) is the standard stain used in histological analysis of tissues in both clinical and research settings. Although H&E is ubiquitous and reveals tissue microanatomy, the classification and mapping of cell subtypes often require the use of specialized stains. The recent CoNIC Challenge focused on artificial intelligence classification of six types of cells on colon H&E but was unable to classify epithelial subtypes (progenitor, enteroendocrine, goblet), lymphocyte subtypes (B, helper T, cytotoxic T), and connective subtypes (fibroblasts). We propose to use inter-modality learning to label previously un-labelable cell types on H&E.ApproachWe took advantage of the cell classification information inherent in multiplexed immunofluorescence (MxIF) histology to create cell-level annotations for 14 subclasses. Then, we performed style transfer on the MxIF to synthesize realistic virtual H&E. We assessed the efficacy of a supervised learning scheme using the virtual H&E and 14 subclass labels. We evaluated our model on virtual H&E and real H&E.ResultsOn virtual H&E, we were able to classify helper T cells and epithelial progenitors with positive predictive values of 0.34±0.15 (prevalence 0.03±0.01) and 0.47±0.1 (prevalence 0.07±0.02), respectively, when using ground truth centroid information. On real H&E, we needed to compute bounded metrics instead of direct metrics because our fine-grained virtual H&E predicted classes had to be matched to the closest available parent classes in the coarser labels from the real H&E dataset. For the real H&E, we could classify bounded metrics for the helper T cells and epithelial progenitors with upper bound positive predictive values of 0.43±0.03 (parent class prevalence 0.21) and 0.94±0.02 (parent class prevalence 0.49) when using ground truth centroid information.ConclusionsThis is the first work to provide cell type classification for helper T and epithelial progenitor nuclei on H&E.

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Proceedings Article | 3 April 2024 Presentation + Paper

Nucleus subtype classification using inter-modality learning

Lucas Remedios, Shunxing Bao, Samuel Remedios, Ho Hin Lee, Leon Cai, Thomas Li, Ruining Deng, Can Cui, Jia Li, Qi Liu, Ken Lau, Joseph Roland, Mary Washington, Lori Coburn, Keith Wilson, Yuankai Huo, Bennett Landman

Proceedings Volume 12933, 129330F (2024) https://doi.org/10.1117/12.3006237

KEYWORDS: Machine learning, Colon, Pathology, Multiplexing, Human physiology, Clinical research, Biological imaging, Artificial intelligence

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Proceedings Article | 2 April 2024 Presentation + Paper

Pushing the limits of zero-shot self-supervised super-resolution of anisotropic MR images

Samuel Remedios, Shuwen Wei, Blake Dewey, Aaron Carass, Dzung Pham, Jerry Prince

Proceedings Volume 12926, 1292606 (2024) https://doi.org/10.1117/12.3007304

KEYWORDS: Magnetic resonance imaging, Super resolution, Machine learning, Interpolation, Wavelets, Image resolution, Deep learning

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Proceedings Article | 2 April 2024 Poster + Paper

Deep-learning-based segmentation of hydrocephalus brain ventricle from ultrasound

Yuli Wang, Yihao Liu, Shuwen Wei, Yuan Xue, Lianrui Zuo, Samuel Remedios, Zhangxing Bian, Michael Meggyesy, Jheesoo Ahn, Ryan Lee, Mark Luciano, Jerry Prince, Aaron Carass

Proceedings Volume 12926, 1292639 (2024) https://doi.org/10.1117/12.3007668

KEYWORDS: Ultrasonography, Image segmentation, Brain, Neuroimaging, Deep learning, Video, Magnetic resonance imaging, Education and training, Computed tomography, Convolution

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Proceedings Article | 2 April 2024 Poster + Paper

AniRes2D: anisotropic residual-enhanced diffusion for 2D MR super-resolution

Zejun Wu, Samuel Remedios, Blake Dewey, Aaron Carass, Jerry Prince

Proceedings Volume 12930, 1293029 (2024) https://doi.org/10.1117/12.3008456

KEYWORDS: Lawrencium, Magnetic resonance imaging, Super resolution, Image processing, Education and training, Diffusion, Image quality, Radon, Data modeling, Denoising

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Proceedings Article | 2 April 2024 Poster + Paper

Self-supervised super-resolution of 2-D pre-clinical MRI acquisitions

Lin Guo, Samuel Remedios, Alexandru Korotcov, Dzung Pham

Proceedings Volume 12930, 129302K (2024) https://doi.org/10.1117/12.3016094

KEYWORDS: Education and training, Magnetic resonance imaging, Super resolution, Interpolation, Animals, Lawrencium, Animal model studies, 3D acquisition, Brain, Image quality

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Proceedings Article | 2 April 2024 Poster + Paper

Harmonization-enriched domain adaptation with light fine-tuning for multiple sclerosis lesion segmentation

Jinwei Zhang, Lianrui Zuo, Blake Dewey, Samuel Remedios, Savannah Hays, Dzung Pham, Jerry Prince, Aaron Carass

Proceedings Volume 12930, 129302I (2024) https://doi.org/10.1117/12.3011291

KEYWORDS: Education and training, Multiple sclerosis, Magnetic resonance imaging, Image segmentation, Solids, Fourier transforms, Scanners, White matter, Regenerative medicine, Neuroscience

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Proceedings Article | 3 April 2023 Poster + Paper

Unsupervised quality assurance for brain MR image rigid registration using latent shape representation

Yuan Xue, Lianrui Zuo, Samuel Remedios, Blake Dewey, Peiyu Duan, Yihao Liu, Rendong Zhang, Scott Newsome, Ellen Mowry, Aaron Carass, Jerry Prince

Proceedings Volume 12464, 124641G (2023) https://doi.org/10.1117/12.2647484

KEYWORDS: Image registration, Magnetic resonance imaging, 3D modeling, Brain, Education and training, Binary data, Rigid registration, Neuroimaging, Image quality, Multiple sclerosis

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Proceedings Article | 3 April 2023 Presentation + Paper

A deep generative prior for high-resolution isotropic MR head slices

Samuel Remedios, Blake Dewey, Aaron Carass, Dzung Pham, Jerry Prince

Proceedings Volume 12464, 124640I (2023) https://doi.org/10.1117/12.2654032

KEYWORDS: Magnetic resonance imaging, Head, Data modeling, Brain, Image resolution, Deep learning, Deep convolutional neural networks

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Proceedings Article | 4 April 2022 Presentation + Paper

Automatic classification of MRI contrasts using a deep Siamese network and one-shot learning

Yiyu Chou, Samuel Remedios, John Butman, Dzung Pham

Proceedings Volume 12032, 120320G (2022) https://doi.org/10.1117/12.2613052

KEYWORDS: Magnetic resonance imaging, Neural networks, Image classification, Brain, Image processing, Neuroimaging, Network architectures, Computer programming, Medical imaging

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Proceedings Article | 4 April 2022 Presentation + Paper

Efficient quality control with mixed CT and CTA datasets

Lucas Remedios, Leon Cai, Colin Hansen, Samuel Remedios, Bennett Landman

Proceedings Volume 12032, 120320E (2022) https://doi.org/10.1117/12.2607406

KEYWORDS: Computed tomography, Traumatic brain injury, Head, Angiography, Neural networks

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Proceedings Article | 4 April 2022 Poster + Paper

Multiple Sclerosis brain lesion segmentation with different architecture ensembles

Pouria Tohidi, Samuel Remedios, Danielle Greenman, Muhan Shao, Shuo Han, Blake Dewey, Jacob Reinhold, Yi-Yu Chou, Dzung Pham, Jerry Prince, Aaron Carass

Proceedings Volume 12036, 1203625 (2022) https://doi.org/10.1117/12.2623302

KEYWORDS: Image segmentation, Magnetic resonance imaging, Head, Brain, Skull, Network architectures, Image filtering

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SPIE Journal Paper | 23 December 2020

Generalizing deep whole-brain segmentation for post-contrast MRI with transfer learning

Camilo Bermudez, Samuel Remedios, Karthik Ramadass, Maureen McHugo, Stephan Heckers, Yuankai Huo, Bennett Landman

JMI, Vol. 7, Issue 06, 064004, (December 2020) https://doi.org/10.1117/12.10.1117/1.JMI.7.6.064004

KEYWORDS: Image segmentation, Magnetic resonance imaging, Data acquisition, Brain, Image processing algorithms and systems, Image processing, Data modeling, Tissues, Medical imaging, Neural networks

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Proceedings Article | 10 March 2020 Presentation + Paper

Generalizing deep whole brain segmentation for pediatric and post-contrast MRI with augmented transfer learning

Camilo Bermudez, Justin Blaber, Samuel Remedios, Jess Reynolds, Catherine Lebel, Maureen McHugo, Stephan Heckers, Yuankai Huo, Bennett Landman

Proceedings Volume 11313, 113130L (2020) https://doi.org/10.1117/12.2548622

KEYWORDS: Image segmentation, Brain, Magnetic resonance imaging, Neuroimaging, Data acquisition, Neural networks, Image processing, Databases, Medical imaging

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Proceedings Article | 10 March 2020 Presentation + Paper

Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection

Samuel Remedios, Zihao Wu, Camilo Bermudez, Cailey Kerley, Snehashis Roy, Mayur Patel, John Butman, Bennett Landman, Dzung Pham

Proceedings Volume 11313, 113130F (2020) https://doi.org/10.1117/12.2549356

KEYWORDS: Data modeling, Computed tomography, Image segmentation, 3D image processing, Medical imaging, Neural networks, Medical research

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Proceedings Article | 15 March 2019 Presentation + Paper

Harmonizing 1.5T/3T diffusion weighted MRI through development of deep learning stabilized microarchitecture estimators

Vishwesh Nath, Samuel Remedios, Prasanna Parvathaneni, Colin Hansen, Roza Bayrak, Camilo Bermudez, Justin Blaber, Kurt Schilling, Vaibhav Janve, Yurui Gao, Yuankai Huo, Ilwoo Lyu, Owen Williams, Susan Resnick, Lori Beason-Held, Baxter Rogers, Iwona Stepniewska, Adam Anderson, Bennett Landman

Proceedings Volume 10949, 109490O (2019) https://doi.org/10.1117/12.2512902

KEYWORDS: Scanners, Network architectures, Diffusion, Magnetic resonance imaging, Brain, Tissues, Diffusion tensor imaging

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Diffusion weighted magnetic resonance imaging (DW-MRI) is interpreted as a quantitative method that is sensitive to tissue microarchitecture at a millimeter scale. However, the sensitization is dependent on acquisition sequences (e.g., diffusion time, gradient strength, etc.) and susceptible to imaging artifacts. Hence, comparison of quantitative DW-MRI biomarkers across field strengths (including different scanners, hardware performance, and sequence design considerations) is a challenging area of research. We propose a novel method to estimate microstructure using DW-MRI that is robust to scanner difference between 1.5T and 3T imaging. We propose to use a null space deep network (NSDN) architecture to model DW-MRI signal as fiber orientation distributions (FOD) to represent tissue microstructure. The NSDN approach is consistent with histologically observed microstructure (on previously acquired ex vivo squirrel monkey dataset) and scan-rescan data. The contribution of this work is that we incorporate identical dual networks (IDN) to minimize the influence of scanner effects via scan-rescan data. Briefly, our estimator is trained on two datasets. First, a histology dataset was acquired on three squirrel monkeys with corresponding DW-MRI and confocal histology (512 independent voxels). Second, 37 control subjects from the Baltimore Longitudinal Study of Aging (67-95 y/o) were identified who had been scanned at 1.5T and 3T scanners (b-value of 700 s/mm² , voxel resolution at 2.2mm, 30-32 gradient volumes) with an average interval of 4 years (standard deviation 1.3 years). After image registration, we used paired white matter (WM) voxels for 17 subjects and 440 histology voxels for training and 20 subjects and 72 histology voxels for testing. We compare the proposed estimator with super-resolved constrained spherical deconvolution (CSD) and a previously presented regression deep neural network (DNN). NSDN outperformed CSD and DNN in angular correlation coefficient (ACC) 0.81 versus 0.28 and 0.46, mean squared error (MSE) 0.001 versus 0.003 and 0.03, and general fractional anisotropy (GFA) 0.05 versus 0.05 and 0.09. Further validation and evaluation with contemporaneous imaging are necessary, but the NSDN is promising avenue for building understanding of microarchitecture in a consistent and deviceindependent manner.