Dr. Antong Chen Profile

Dr. Antong Chen

Director of Data Science at Merck & Co Inc

SPIE Involvement:

Conference Program Committee | Author

Publications (18)

Proceedings Article | 3 April 2024 Presentation + Paper

HER2 prediction from breast H&E-stained whole slide images: a comparison between ROI-based supervised learning and multiple-instance learning approaches

Tosha Shah, Lin Li, Antong Chen, Radha Krishnan, Rajath Soans, Amir Vajdi, Razvan Cristescu

Proceedings Volume 12933, 129330R (2024) https://doi.org/10.1117/12.3006884

KEYWORDS: Education and training, Tumor growth modeling, Performance modeling, Machine learning, Breast, Data modeling, Tumors, Feature extraction, Breast cancer, Visual process modeling

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Proceedings Article | 3 April 2024 Presentation + Paper

Automated anomaly detection in histology images using deep learning

Lillie Shelton, Rajath Soans, Tosha Shah, Thomas Forest, Kyathanahalli Janardhan, Michael Napolitano, Raymond Gonzalez, Grady Carlson, Jyoti Shah, Antong Chen

Proceedings Volume 12933, 129330N (2024) https://doi.org/10.1117/12.3006224

KEYWORDS: Tissues, Data modeling, Visualization, Deep learning, Safety, Pathology

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Proceedings Article | 2 April 2024 Presentation + Paper

Universal 3D CT lesion segmentation using SAM with RECIST annotation

Yiqiao Liu, Sarah Halek, Lin Li, Michal Tomaszewski, Shubing Wang, Richard Baumgartner, Jianda Yuan, Gregory Goldmacher, Antong Chen

Proceedings Volume 12926, 1292605 (2024) https://doi.org/10.1117/12.3006527

KEYWORDS: Deep learning, Oncology, Image segmentation, Computed tomography

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Proceedings Article | 2 April 2024 Presentation + Paper

Deep learning-based automated lung tumor segmentation in mouse preclinical micro-CT scans with limited annotations

Gain Robinson, Lin Li, Yimin Zhu, Rachel DuPont, Laura Engstrom, Laxminarayan Hegde, Jie Zhang-Hoover, Kimberly Bettano, Antong Chen

Proceedings Volume 12930, 1293006 (2024) https://doi.org/10.1117/12.3006521

KEYWORDS: Tumors, Lung, Chest, Image segmentation, Heart, Process modeling, Data modeling, Tissues, Voxels, Deep learning

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Proceedings Article | 7 April 2023 Presentation + Paper

Deep learning-based rapid macrophage cell detection and localization in high-content microscopy screening

Kristen Ong, Xuezhu Cai, Vasant Marur, Veronica Soloveva, Uwe Mueller, Antong Chen

Proceedings Volume 12471, 1247117 (2023) https://doi.org/10.1117/12.2654312

KEYWORDS: Object detection, Microscopy, Image processing, Image segmentation, Profiling, Deep learning

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Proceedings Article | 4 April 2022 Poster + Paper

Prediction of tumor mutation burden from H&E whole-slide images: a comparison of training strategies with convolutional neural networks

Antong Chen, Tosha Shah, Andrew Brown, Haleh Akrami, Albert Swiston, Amir Vajdi, Radha Krishnan, Razvan Cristescu

Proceedings Volume 12039, 120391D (2022) https://doi.org/10.1117/12.2613196

KEYWORDS: Tumors, Melanoma, Convolutional neural networks, Tissues, Lung cancer, Cancer, Tumor growth modeling, Image classification, Receivers, Oncology

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Proceedings Article | 15 February 2021 Poster + Paper

Automated localization and segmentation of vertebrae in the micro-CT images of rabbit fetuses using 3D Convolutional Neural Networks

Antong Chen, Saideep Gona, Dahai Xue, Tosha Shah, Alexa Gleason, Barbara Robinson, Britta Mattson, Catherine Hines

Proceedings Volume 11596, 1159624 (2021) https://doi.org/10.1117/12.2581117

KEYWORDS: Image segmentation, Convolutional neural networks, 3D image processing, Bone, Spine, Toxicology, Convolution

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Proceedings Article | 10 March 2020 Paper

Comparison of training strategies for the segmentation of retina layers in optical coherence tomography images of rodent eyes using convolutional neural networks

Antong Chen, Charlene Zhi Lin Ong, Weiwei Luo, Chen Fei Lee, Ser Mien Chia, Joana Galvao, Daniel Metzger, Eric Gifford, Chih-Liang Chin, Asad Abu Bakar Ali

Proceedings Volume 11313, 1131339 (2020) https://doi.org/10.1117/12.2549442

KEYWORDS: Image segmentation, Retina, Optical coherence tomography, Data modeling, Convolutional neural networks, Eye, Animal model studies, Performance modeling, Distortion, Visual process modeling

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Proceedings Article | 15 March 2019 Presentation + Paper

Localization and labeling of cervical vertebral bones in the micro-CT images of rabbit fetuses using a 3D deep convolutional neural network

Antong Chen, Dahai Xue, Tosha Shah, Catherine D. Hines, Alexa Gleason, Manishkumar Patel, Barbara Robinson, Britta Mattson, Belma Dogdas

Proceedings Volume 10949, 1094913 (2019) https://doi.org/10.1117/12.2513108

KEYWORDS: Bone, Image segmentation, 3D image processing, Convolutional neural networks, Fetus, Computed tomography, Toxicology

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Proceedings Article | 2 March 2018 Presentation + Paper

Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional LSTM

Dongqing Zhang, Ilknur Icke, Belma Dogdas, Sarayu Parimal, Smita Sampath, Joseph Forbes, Ansuman Bagchi, Chih-Liang Chin, Antong Chen

Proceedings Volume 10574, 105740A (2018) https://doi.org/10.1117/12.2293984

KEYWORDS: Image segmentation, Convolutional neural networks, Magnetic resonance imaging, Neural networks, Feature extraction, Machine learning, Pattern recognition

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Proceedings Article | 24 February 2017 Paper

Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning

Tian Zhou, Ilknur Icke, Belma Dogdas, Sarayu Parimal, Smita Sampath, Joseph Forbes, Ansuman Bagchi, Chih-Liang Chin, Antong Chen

Proceedings Volume 10133, 101331W (2017) https://doi.org/10.1117/12.2253963

KEYWORDS: Neural networks, Image segmentation, Magnetic resonance imaging, Data modeling, Databases, Cardiovascular magnetic resonance imaging, Machine learning, Pattern recognition, Heart, Mathematical modeling, Performance modeling, Mathematics, Image processing, Information technology

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Proceedings Article | 18 March 2016 Paper

Image-guided intracranial cannula placement for awake in vivo microdialysis in nonhuman primates

Antong Chen, Ashleigh Bone, Catherine D. Hines, Belma Dogdas, Tamara Montgomery, Maria Michener, Christopher Winkelmann, Soheil Ghafurian, Laura Lubbers, John Renger, Ansuman Bagchi, Jason Uslaner, Colena Johnson, Hatim Zariwala

Proceedings Volume 9786, 978620 (2016) https://doi.org/10.1117/12.2217539

KEYWORDS: Brain, Magnetic resonance imaging, Computed tomography, Skull, Neuroimaging, Custom fabrication, Computer aided design, In vivo imaging, Psychiatry, Neurodegeneration, Surgery, Computer aided diagnosis and therapy

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Intracranial microdialysis is used for sampling neurochemicals and large peptides along with their metabolites from the interstitial fluid (ISF) of the brain. The ability to perform this in nonhuman primates (NHP) e.g., rhesus could improve the prediction of pharmacokinetic (PK) and pharmacodynamics (PD) action of drugs in human. However, microdialysis in rhesus brains is not as routinely performed as in rodents. One challenge is that the precise intracranial probe placement in NHP brains is difficult due to the richness of the anatomical structure and the variability of the size and shape of brains across animals. Also, a repeatable and reproducible ISF sampling from the same animal is highly desirable when combined with cognitive behaviors or other longitudinal study end points. Toward that end, we have developed a semi-automatic flexible neurosurgical method employing MR and CT imaging to (a) derive coordinates for permanent guide cannula placement in mid-brain structures and (b) fabricate a customized recording chamber to implant above the skull for enclosing and safeguarding access to the cannula for repeated experiments. In order to place the intracranial guide cannula in each subject, the entry points in the skull and the depth in the brain were derived using co-registered images acquired from MR and CT scans. The anterior/posterior (A/P) and medial-lateral (M/L) rotation in the pose of the animal was corrected in the 3D image to appropriately represent the pose used in the stereotactic frame. An array of implanted fiducial markers was used to transform stereotactic coordinates to the images. The recording chamber was custom fabricated using computer-aided design (CAD), such that it would fit the contours of the individual skull with minimum error. The chamber also helped in guiding the cannula through the entry points down a trajectory into the depth of the brain. We have validated our method in four animals and our results indicate average placement error of cannula to be 1.20 ± 0.68 mm of the targeted positions. The approach employed here for derivation of the coordinates, surgical implantation and post implant validation is built using traditional access to surgical and imaging methods without the necessity of intra-operative imaging. The validation of our method lends support to its wider application in most nonhuman primate laboratories with onsite MR and CT imaging capabilities.

Proceedings Article | 18 March 2016 Paper

Automatic pose correction for image-guided nonhuman primate brain surgery planning

Soheil Ghafurian, Antong Chen, Catherine Hines, Belma Dogdas, Ashleigh Bone, Kenneth Lodge, Stacey O'Malley, Christopher Winkelmann, Ansuman Bagchi, Laura Lubbers, Jason Uslaner, Colena Johnson, John Renger, Hatim Zariwala

Proceedings Volume 9786, 97860O (2016) https://doi.org/10.1117/12.2217534

KEYWORDS: Surgery, Image registration, Brain, Magnetic resonance imaging, 3D image processing, Neuroimaging, Image-guided intervention, Rigid registration, 3D acquisition, Computed tomography, Head, Ear

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Proceedings Article | 18 March 2015 Paper

Targeting of deep-brain structures in nonhuman primates using MR and CT Images

Antong Chen, Catherine Hines, Belma Dogdas, Ashleigh Bone, Kenneth Lodge, Stacey O’Malley, Brett Connolly, Christopher Winkelmann, Ansuman Bagchi, Laura Lubbers, Jason Uslaner, Colena Johnson, John Renger, Hatim Zariwala

Proceedings Volume 9415, 94152K (2015) https://doi.org/10.1117/12.2081620

KEYWORDS: Magnetic resonance imaging, Computed tomography, Brain, Genetics, Image registration, Skull, Animal model studies, 3D acquisition, Rigid registration, Surgery

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Proceedings Article | 13 March 2014 Paper

Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

Antong Chen, Belma Dogdas, Saurin Mehta, Ansuman Bagchi, L. David Wise, Christopher Winkelmann

Proceedings Volume 9038, 903813 (2014) https://doi.org/10.1117/12.2043740

KEYWORDS: Bone, Skull, Image segmentation, Image registration, Image processing, Rigid registration, Image classification, Inspection, Error analysis, Fetus

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Proceedings Article | 14 February 2012 Paper

Segmentation of parotid glands in head and neck CT images using a constrained active shape model with landmark uncertainty

Antong Chen, Jack Noble, Kenneth Niermann, Matthew Deeley, Benoit Dawant

Proceedings Volume 8314, 83140P (2012) https://doi.org/10.1117/12.911534

KEYWORDS: Image segmentation, Head, Neck, Computed tomography, 3D modeling, Expectation maximization algorithms, Data modeling, Radiotherapy, Tissues, Distance measurement

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Proceedings Article | 12 March 2011 Paper

Evaluation of multi atlas-based approaches for the segmentation of the thyroid gland in IMRT head-and-neck CT images

Antong Chen, Kenneth Niermann, Matthew Deeley, Benoit Dawant

Proceedings Volume 7962, 796224 (2011) https://doi.org/10.1117/12.878246

KEYWORDS: Image segmentation, Computed tomography, Head, Neck, Image registration, Image processing, Expectation maximization algorithms, Binary data, Algorithm development, Blood vessels

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Proceedings Article | 13 March 2010 Paper

Segmentation of lymph node regions in head-and-neck CT images using a combination of registration and active shape model

Antong Chen, Matthew Deeley, Kenneth Niermann, Luigi Moretti, Benoit Dawant

Proceedings Volume 7623, 76231Q (2010) https://doi.org/10.1117/12.843823

KEYWORDS: Image segmentation, Image registration, Lymphatic system, Computed tomography, Neck, Head, Image processing, Bone, 3D modeling, Tumor growth modeling

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