Dr. Jordan D. Fuhrman Profile

Dr. Jordan D. Fuhrman

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Publications (19)

SPIE Journal Paper | 2 May 2024 Open Access

Computerized assessment of background parenchymal enhancement on breast dynamic contrast-enhanced-MRI including electronic lesion removal

Lindsay Douglas, Jordan Fuhrman, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Hiroyuki Abe, Maryellen Giger

JMI, Vol. 11, Issue 03, 034501, (May 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.3.034501

KEYWORDS: Breast, Image segmentation, Education and training, Visualization, Magnetic resonance imaging, Tumors, Statistical analysis, Binary data, Image classification, Breast cancer

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PurposeCurrent clinical assessment qualitatively describes background parenchymal enhancement (BPE) as minimal, mild, moderate, or marked based on the visually perceived volume and intensity of enhancement in normal fibroglandular breast tissue in dynamic contrast-enhanced (DCE)-MRI. Tumor enhancement may be included within the visual assessment of BPE, thus inflating BPE estimation due to angiogenesis within the tumor. Using a dataset of 426 MRIs, we developed an automated method to segment breasts, electronically remove lesions, and calculate scores to estimate BPE levels.ApproachA U-Net was trained for breast segmentation from DCE-MRI maximum intensity projection (MIP) images. Fuzzy c-means clustering was used to segment lesions; the lesion volume was removed prior to creating projections. U-Net outputs were applied to create projection images of both, affected, and unaffected breasts before and after lesion removal. BPE scores were calculated from various projection images, including MIPs or average intensity projections of first- or second postcontrast subtraction MRIs, to evaluate the effect of varying image parameters on automatic BPE assessment. Receiver operating characteristic analysis was performed to determine the predictive value of computed scores in BPE level classification tasks relative to radiologist ratings.ResultsStatistically significant trends were found between radiologist BPE ratings and calculated BPE scores for all breast regions (Kendall correlation, p<0.001). Scores from all breast regions performed significantly better than guessing (p<0.025 from the z-test). Results failed to show a statistically significant difference in performance with and without lesion removal. BPE scores of the affected breast in the second postcontrast subtraction MIP after lesion removal performed statistically greater than random guessing across various viewing projections and DCE time points.ConclusionsResults demonstrate the potential for automatic BPE scoring to serve as a quantitative value for objective BPE level classification from breast DCE-MR without the influence of lesion enhancement.

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Proceedings Article | 3 April 2024 Presentation + Paper

Using artificial intelligence for chest radiograph interpretation: a retrospective multi-reader-multi-case (MRMC) study of the automatic detection of multiple abnormalities and generation of diagnostic report system

Lin Guo, Guanxun Cheng, Lifei Wang, Bin Zheng, Stefan Jaeger, Maryellen Giger, Jordan Fuhrman, Hui Li, Ajay Divekar, Qian Xiao, Lingjun Qian, Li Xia, Hongjun Li, Fleming Y. Lure

Proceedings Volume 12927, 129270Y (2024) https://doi.org/10.1117/12.3005136

KEYWORDS: Chest imaging, Diagnostics, Artificial intelligence, Pulmonary disorders, Lung, Image fusion, Diseases and disorders, Deep learning, Clinical practice

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Proceedings Article | 3 April 2024 Poster + Paper

Temporal assessment of magnetic resonance imaging radiomic features to predict renal function decline in patients with autosomal dominant polycystic kidney disease

Linnea Kremer, Madison Orlins, John Trevino, Jordan Fuhrman, Arlene Chapman, Sam Armato

Proceedings Volume 12927, 1292731 (2024) https://doi.org/10.1117/12.3008506

KEYWORDS: Radiomics, Kidney, Diseases and disorders, Magnetic resonance imaging, Feature extraction, Image segmentation

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Proceedings Article | 3 April 2024 Poster

Quantification, model characterization, and challenges in automatic perivascular space candidate discrimination

Jordan Fuhrman, Madison Luther, Ali Mansour, Laura Dennis, Fernando Goldenberg, Maryellen Giger, Juan Piantino

Proceedings Volume 12927, 129272G (2024) https://doi.org/10.1117/12.3006493

KEYWORDS: Magnetic resonance imaging, Evolutionary algorithms, Cognitive modeling, Binary data, Traumatic brain injury, Image segmentation, Dementia, Deep learning, Brain, Artificial intelligence

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SPIE Journal Paper | 20 November 2023 Open Access

U-Net breast lesion segmentations for breast dynamic contrast-enhanced magnetic resonance imaging

Lindsay Douglas, Roma Bhattacharjee, Jordan Fuhrman, Karen Drukker, Qiyuan Hu, Alexandra Edwards, Deepa Sheth, Maryellen Giger

JMI, Vol. 10, Issue 06, 064502, (November 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.6.064502

KEYWORDS: Image segmentation, Breast, 3D image processing, 3D imaging standards, Magnetic resonance imaging, Education and training, Cross validation, 3D modeling, 3D image enhancement, Artificial intelligence

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SPIE Journal Paper | 21 August 2023 Open Access

Predicting intensive care need for COVID-19 patients using deep learning on chest radiography

Hui Li, Karen Drukker, Qiyuan Hu, Heather Whitney, Jordan Fuhrman, Maryellen Giger

JMI, Vol. 10, Issue 04, 044504, (August 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044504

KEYWORDS: COVID 19, Chest imaging, Data modeling, Deep learning, Education and training, Performance modeling, Radiography, Medical imaging, Machine learning, Diseases and disorders

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Proceedings Article | 10 April 2023 Presentation + Paper

User experience evaluation for MIDRC AI interface

Naveena Gorre, Jordan Fuhrman, Eduardo Carranza, Hui Li, Ravi Madduri, Maryellen Giger, Issam El Naqa

Proceedings Volume 12469, 1246904 (2023) https://doi.org/10.1117/12.2651812

KEYWORDS: Artificial intelligence, Data modeling, Machine learning, Visualization, Medical imaging, Visual process modeling, Human-machine interfaces, Decision making, Evolutionary algorithms

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Proceedings Article | 7 April 2023 Presentation + Paper

Assessing an AI-based smart imagery framing and truthing (SIFT) system to assist radiologists annotating lung abnormalities on chest x-ray images for development of deep learning models

Lin Guo, Kunlei Hong, Ziqi Zhang, Bin Zheng, Stefan Jaeger, Jordan Fuhrman, Hui Li, Maryellen Giger, Andrei Gabrielian, Alex Rosenthal, Darrell Hurt, Ziv Yaniv, Y. M. Fleming Lure

Proceedings Volume 12465, 124650R (2023) https://doi.org/10.1117/12.2653826

KEYWORDS: Education and training, Deep learning, Chest imaging, Artificial intelligence, Diseases and disorders, Image segmentation, Data modeling, COVID 19, Machine learning, Lung

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Proceedings Article | 4 April 2022 Presentation

Multiple instance learning for automatic emphysema evaluation on low-dose CT lung cancer screening scans

Jordan Fuhrman, Yeqing Zhu, Rowena Yip, Feng Li, Artit Jirapatnakul, Claudia Henschke, David Yankelevitz, Maryellen Giger

Proceedings Volume PC12033, PC1203305 (2022) https://doi.org/10.1117/12.2612768

KEYWORDS: Emphysema, Lung cancer, Computed tomography, 3D modeling, Visualization, Statistical analysis, Machine learning, Cancer, 3D scanning, 3D imaging standards

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SPIE Journal Paper | 1 October 2021 Open Access

Lessons learned in transitioning to AI in the medical imaging of COVID-19

Issam El Naqa, Qiyuan Hu, Weijie Chen, Hui Li, Jordan Fuhrman, Naveena Gorre, Maryellen Giger

JMI, Vol. 8, Issue S1, 010902, (October 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.010902

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Proceedings Article | 5 March 2021 Presentation + Paper

Comparing Mask R-CNN and U-Net architectures for robust automatic segmentation of immune cells in immunofluorescence images of Lupus Nephritis biopsies

Madeleine Durkee, Rebecca Abraham, Junting Ai, Jordan Fuhrman, Marcus Clark, Maryellen Giger

Proceedings Volume 11647, 116470X (2021) https://doi.org/10.1117/12.2577785

KEYWORDS: Image segmentation, Biopsy, Therapeutics, Microscopes, Luminescence, Kidney, Inflammation, Convolutional neural networks, Confocal microscopy

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Proceedings Article | 5 March 2021 Presentation

Application of U-Nets for the automatic segmentation of immune cells in high-dimensional microscopy images of kidney biopsies

Rebecca Abraham, Madeleine Durkee, Margaret Veselits, Junting Ai, Jordan Fuhrman, Marcus Clark, Maryellen Giger

Proceedings Volume 11647, 116470N (2021) https://doi.org/10.1117/12.2577788

KEYWORDS: Image segmentation, Kidney, Biopsy, Microscopy, Tissues, Machine vision, Luminescence, Data mining, Convolutional neural networks, Confocal microscopy

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Application and generalizability of U-Net segmentation of immune cells in inflamed tissue

Rebecca Abraham, Madeleine Durkee, Margaret Veselits, Junting Ai, Jordan Fuhrman, Marcus Clark, Maryellen Giger

Proceedings Volume 11603, 1160317 (2021) https://doi.org/10.1117/12.2581063

KEYWORDS: Tissues, Image segmentation, Machine vision, Biopsy, Visualization, Tumors, Pathology, Machine learning, Kidney, Confocal microscopy

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Proceedings Article | 15 February 2021 Poster + Presentation + Paper

Radiomic texture analysis for the assessment of osteoporosis on low-dose thoracic CT scans

Jordan Fuhrman, Linnea Kremer, Yeqing Zhu, Rowena Yip, Feng Li, Li Lan, Hui Li, Artit Jirapatnakul, Claudia Henschke, David Yankelevitz, Maryellen Giger

Proceedings Volume 11597, 1159730 (2021) https://doi.org/10.1117/12.2582031

KEYWORDS: Computed tomography, Visualization, Lung cancer, Feature extraction, Visual analytics, Minerals, Databases, Bone

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Proceedings Article | 15 February 2021 Presentation + Paper

Comparison of 2D and 3D U-Net breast lesion segmentations on DCE-MRI

Roma Bhattacharjee, Lindsay Douglas, Karen Drukker, Qiyuan Hu, Jordan Fuhrman, Deepa Sheth, Maryellen Giger

Proceedings Volume 11597, 115970D (2021) https://doi.org/10.1117/12.2581846

KEYWORDS: Image segmentation, Breast, Magnetic resonance imaging, Image processing algorithms and systems, Feature extraction, Volume rendering, Medical imaging, Fuzzy logic, Convolutional neural networks, Computer aided diagnosis and therapy

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SPIE Journal Paper | 29 December 2020 Open Access

Cascaded deep transfer learning on thoracic CT in COVID-19 patients treated with steroids

Fleming Lure, Zhe Luo, Maryellen Giger, Jordan Fuhrman, Zegang Dong, Jun Chen

JMI, Vol. 8, Issue S1, 014501, (December 2020) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.014501

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Proceedings Article | 16 March 2020 Presentation + Paper

Effect of observer variability and training cases on U-Net segmentation performance

Jordan Fuhrman, Peter Halloran, Rowena Yip, Artit Jirapatnakul, Claudia Henschke, David Yankelevitz, Maryellen Giger

Proceedings Volume 11316, 113160T (2020) https://doi.org/10.1117/12.2549118

KEYWORDS: Image segmentation, Medical imaging, Binary data, Computed tomography, Radiology, Heart, Medical physics, Medicine, Network architectures, Medical research

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Proceedings Article | 16 March 2020 Presentation + Paper

Cascade of U-Nets in the detection and classification of coronary artery calcium in thoracic low-dose CT

Jordan Fuhrman, Rowena Yip, Artit Jirapatnakul, Claudia Henschke, David Yankelevitz, Maryellen Giger

Proceedings Volume 11314, 113140A (2020) https://doi.org/10.1117/12.2549117

KEYWORDS: Image segmentation, Arteries, Heart, Image classification, Calcium, Aorta, Computed tomography, Medical imaging, Network architectures, Radiology

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Proceedings Article | 13 March 2019 Paper

Detection and classification of coronary artery calcifications in low dose thoracic CT using deep learning

Jordan Fuhrman, Jennie Crosby, Rowena Yip, Claudia Henschke, David Yankelevitz, Maryellen Giger

Proceedings Volume 10950, 1095039 (2019) https://doi.org/10.1117/12.2513134

KEYWORDS: Arteries, Computed tomography, Feature extraction, Image classification, Diagnostics, Image quality

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Showing 5 of 19 publications

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