Dr. Ho Hin Lee Profile

Dr. Ho Hin Lee

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Author

Area of Expertise:

Deep Learning , Medical Image Analysis , Computer Vision

Publications (25)

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 Poster + Paper

Predicting age from white matter diffusivity with residual learning

Chenyu Gao, Michael Kim, Ho Hin Lee, Qi Yang, Nazirah Mohd Khairi, Praitayini Kanakaraj, Nancy Newlin, Derek Archer, Angela Jefferson, Warren Taylor, Brian Boyd, Lori Beason-Held, Susan Resnick, Yuankai Huo, Katherine Van Schaik, Kurt Schilling, Daniel Moyer, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129262I (2024) https://doi.org/10.1117/12.3006525

KEYWORDS: Brain, Diffusion tensor imaging, Data modeling, White matter, Neuroimaging, Deep learning, Alzheimer disease, Convolutional neural networks, Computer vision technology, Image registration

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Imaging findings inconsistent with those expected at specific chronological age ranges may serve as early indicators of neurological disorders and increased mortality risk. Estimation of chronological age, and deviations from expected results, from structural magnetic resonance imaging (MRI) data has become an important proxy task for developing biomarkers that are sensitive to such deviations. Complementary to structural analysis, diffusion tensor imaging (DTI) has proven effective in identifying age-related microstructural changes within the brain white matter, thereby presenting itself as a promising additional modality for brain age prediction. Although early studies have sought to harness DTI’s advantages for age estimation, there is no evidence that the success of this prediction is owed to the unique microstructural and diffusivity features that DTI provides, rather than the macrostructural features that are also available in DTI data. Therefore, we seek to develop white-matter-specific age estimation to capture deviations from normal white matter aging. Specifically, we deliberately disregard the macrostructural information when predicting age from DTI scalar images, using two distinct methods. The first method relies on extracting only microstructural features from regions of interest (ROIs). The second applies 3D residual neural networks (ResNets) to learn features directly from the images, which are nonlinearly registered and warped to a template to minimize macrostructural variations. When tested on unseen data, the first method yields mean absolute error (MAE) of 6.11 ± 0.19 years for cognitively normal participants and MAE of 6.62 ± 0.30 years for cognitively impaired participants, while the second method achieves MAE of 4.69 ± 0.23 years for cognitively normal participants and MAE of 4.96 ± 0.28 years for cognitively impaired participants. We find that the ResNet model captures subtler, non-macrostructural features for brain age prediction.

Proceedings Article | 2 April 2024 Presentation + Paper

Inter-vendor harmonization of CT reconstruction kernels using unpaired image translation

Aravind Krishnan, Kaiwen Xu, Thomas Li, Chenyu Gao, Lucas Remedios, Praitayini Kanakaraj, Ho Hin Lee, Shunxing Bao, Kim Sandler, Fabien Maldonado, Ivana Išgum, Bennett Landman

Proceedings Volume 12926, 129261D (2024) https://doi.org/10.1117/12.3006608

KEYWORDS: Emphysema, Bone, CT reconstruction, Computed tomography, Data modeling, Lung, Image analysis, Medical image reconstruction

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Proceedings Article | 2 April 2024 Poster + Paper

Characterizing low-cost registration for photographic images to computed tomography

Michael Kim, Ho Hin Lee, Karthik Ramadass, Chenyu Gao, Katherine Van Schaik, Eric Tkaczyk, Jeffrey Spraggins, Daniel Moyer, Bennett Landman

Proceedings Volume 12930, 1293025 (2024) https://doi.org/10.1117/12.3005578

KEYWORDS: Photogrammetry, Computed tomography, Cameras, Video, Natural surfaces, Photography, Image registration, Tissues, 3D image processing

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Proceedings Article | 2 April 2024 Presentation + Paper

Enhancing hierarchical transformers for whole brain segmentation with intracranial measurements integration

Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Yuankai Huo, Bennett Landman

Proceedings Volume 12930, 129300K (2024) https://doi.org/10.1117/12.3009084

KEYWORDS: Brain, Image segmentation, Transformers, Neuroimaging, Brain mapping, 3D modeling, Magnetic resonance imaging

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SPIE Journal Paper | 2 April 2024 Open Access

Deep conditional generative model for longitudinal single-slice abdominal computed tomography harmonization

Xin Yu, Qi Yang, Yucheng Tang, Riqiang Gao, Shunxing Bao, Leon Cai, Ho Hin Lee, Yuankai Huo, Ann Zenobia Moore, Luigi Ferrucci, Bennett Landman

JMI, Vol. 11, Issue 02, 024008, (April 2024) https://doi.org/10.1117/12.10.1117/1.JMI.11.2.024008

KEYWORDS: Data modeling, Education and training, Computed tomography, Performance modeling, 3D modeling, Tissues, Gallium nitride, Body composition, 3D image processing, 3D acquisition

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SPIE Journal Paper | 18 July 2023 Open Access

Quantifying emphysema in lung screening computed tomography with robust automated lobe segmentation

Thomas Z. Li, Ho Hin Lee, Kaiwen Xu, Riqiang Gao, Benoit M. Dawant, Fabien Maldonado, Kim L. Sandler, Bennett A. Landman

JMI, Vol. 10, Issue 04, 044002, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044002

KEYWORDS: Emphysema, Lung, Lung cancer, Computed tomography, Chronic obstructive pulmonary disease, Algorithm development, Image segmentation, Education and training, 3D modeling, Data modeling

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SPIE Journal Paper | 12 July 2023 Open Access

Single slice thigh CT muscle group segmentation with domain adaptation and self-training

Qi Yang, Xin Yu, Ho Hin Lee, Leon Cai, Kaiwen Xu, Shunxing Bao, Yuankai Huo, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett Landman

JMI, Vol. 10, Issue 04, 044001, (July 2023) https://doi.org/10.1117/12.10.1117/1.JMI.10.4.044001

KEYWORDS: Muscles, Image segmentation, Computed tomography, Magnetic resonance imaging, Education and training, Anatomy, Bone, Gallium nitride, Data modeling, Adversarial training

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Proceedings Article | 7 April 2023 Presentation + Paper

Topological-preserving membrane skeleton segmentation in multiplex immunofluorescence imaging

Shunxing Bao, Can Cui, Jia Li, Yucheng Tang, Ho Hin Lee, Ruining Deng, Lucas Remedios, Xin Yu, Qi Yang, Sophie Chiron, Nathan Heath Patterson, Ken Lau, Qi Liu, Joseph Roland, Lori Coburn, Keith Wilson, Bennett Landman, Yuankai Huo

Proceedings Volume 12471, 124710B (2023) https://doi.org/10.1117/12.2654087

KEYWORDS: Image segmentation, Deep learning, Education and training, Tissues, Biological imaging, RGB color model, Medicine, Image enhancement, Diseases and disorders, Cross validation

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Proceedings Article | 3 April 2023 Poster + Paper

Longitudinal variability analysis on low-dose abdominal CT with deep learning-based segmentation

Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Riqiang Gao, Shunxing Bao, Ann Zenobia Moore, Luigi Ferrucci, Bennett Landman

Proceedings Volume 12464, 1246423 (2023) https://doi.org/10.1117/12.2653762

KEYWORDS: Image segmentation, Body composition, Computed tomography

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Proceedings Article | 3 April 2023 Poster + Paper

Unsupervised registration refinement for generating unbiased eye atlas

Ho Hin Lee, Yucheng Tang, Shunxing Bao, Qi Yang, Xin Yu, Kevin Schey, Jeffery Spraggins, Yuankai Huo, Bennett Landman

Proceedings Volume 12464, 1246422 (2023) https://doi.org/10.1117/12.2653753

KEYWORDS: Eye, Image registration, Deformation, Anatomy, Head, Computed tomography, Medical imaging

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SPIE Journal Paper | 19 May 2022 Open Access

Label efficient segmentation of single slice thigh CT with two-stage pseudo labels

Qi Yang, Xin Yu, Ho Hin Lee, Yucheng Tang, Shunxing Bao, Kristofer Gravenstein, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett A. Landman

JMI, Vol. 9, Issue 05, 052405, (May 2022) https://doi.org/10.1117/12.10.1117/1.JMI.9.5.052405

KEYWORDS: Image segmentation, Bone, Computed tomography, Tissues, Data modeling, Neural networks, Medical imaging, 3D modeling, Performance modeling, Surgery

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Proceedings Article | 4 April 2022 Presentation + Paper

Inpainting missing tissue in multiplexed immunofluorescence imaging

Shunxing Bao, Yucheng Tang, Ho Hin Lee, Riqiang Gao, Qi Yang, Xin Yu, Sophie Chiron, Lori Coburn, Keith Wilson, Joseph Roland, Bennett Landman, Yuankai Huo

Proceedings Volume 12039, 120390K (2022) https://doi.org/10.1117/12.2611827

KEYWORDS: Tissues, Image segmentation, Gallium nitride, Biopsy, Multiplexing, Medical imaging, Medicine, Surgery, Shape memory alloys, Process modeling

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Proceedings Article | 4 April 2022 Poster + Paper

Supervised deep generation of high-resolution arterial phase computed tomography kidney substructure atlas

Ho Hin Lee, Yucheng Tang, Shunxing Bao, Qi Yang, Xin Yu, Agnes Fogo, Raymond Harris, Mark de Caestecker, Jeffery Spraggins, Mattias Heinrich, Yuankai Huo, Bennett Landman

Proceedings Volume 12032, 120322S (2022) https://doi.org/10.1117/12.2608290

KEYWORDS: Kidney, Image registration, Computed tomography, Image segmentation, Diffusion, 3D modeling, Visualization, Machine learning, Spatial learning

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The Human BioMolecular Atlas Program (HuBMAP) provides an opportunity to contextualize findings across cellular to organ systems levels. Constructing an atlas target is the primary endpoint for generalizing anatomical information across scales and populations. An initial target of HuBMAP is the kidney organ and arterial phase contrast-enhanced computed tomography (CT) provides distinctive appearance and anatomical context on the internal substructure of kidney organs such as renal context, medulla, and pelvicalyceal system. With the confounding effects of demographics and morphological characteristics of the kidney across large-scale imaging surveys, substantial variation is demonstrated with the internal substructure morphometry and the intensity contrast due to the variance of imaging protocols. Such variability increases the level of difficulty to localize the anatomical features of the kidney substructure in a well-defined spatial reference for clinical analysis. In order to stabilize the localization of kidney substructures in the context of this variability, we propose a high-resolution CT kidney substructure atlas template. Briefly, we introduce a deep learning preprocessing technique to extract the volumetric interest of the abdominal regions and further perform a deep supervised registration pipeline to stably adapt the anatomical context of the kidney internal substructure. To generate and evaluate the atlas template, arterial phase CT scans of 500 control subjects are de-identified and registered to the atlas template with a complete end-to-end pipeline. With stable registration to the abdominal wall and kidney organs, the internal substructure of both left and right kidneys are substantially localized in the high-resolution atlas space. The atlas average template successfully demonstrated the contextual details of the internal structure and was applicable to generalize the morphological variation of internal substructure across patients.

Proceedings Article | 4 April 2022 Presentation + Paper

Quantification of muscle, bones, and fat on single slice thigh CT

Qi Yang, Xin Yu, Ho Hin Lee, Yucheng Tang, Shunxing Bao, Kristofer Gravenstein, Ann Zenobia Moore, Sokratis Makrogiannis, Luigi Ferrucci, Bennett Landman

Proceedings Volume 12032, 120321K (2022) https://doi.org/10.1117/12.2611664

KEYWORDS: Image segmentation, Bone, Tissues, Data modeling, Computed tomography, Medical imaging, Computer science, Neural networks

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Proceedings Article | 4 April 2022 Poster + Paper

Accelerating 2D abdominal organ segmentation with active learning

Xin Yu, Yucheng Tang, Qi Yang, Ho Hin Lee, Shunxing Bao, Ann Zenobia Moore, Luigi Ferrucci, Bennett Landman

Proceedings Volume 12032, 120323F (2022) https://doi.org/10.1117/12.2611595

KEYWORDS: Image segmentation, Data modeling, Computed tomography, Image processing, Tissues, Visualization

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Proceedings Article | 15 February 2021 Presentation + Paper

A cross-platform informatics system for the Gut Cell Atlas: integrating clinical, anatomical and histological data

Shunxing Bao, Sophie Chiron, Yucheng Tang, Cody Heiser, Austin Southard-Smith, Ho Hin Lee, Marisol Ramirez, Yuankai Huo, Mary Washington, Elizabeth Scoville, Joseph Roland, Qi Liu, Ken Lau, Keith Wilson, Lori Coburn, Bennett Landman

Proceedings Volume 11601, 1160106 (2021) https://doi.org/10.1117/12.2581074

KEYWORDS: Information science, Tissues, System integration, Profiling, Electronic health records, Data storage, Biopsy, Analytical research

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Proceedings Article | 15 February 2021 Presentation + Paper

Construction of a multi-phase contrast computed tomography kidney atlas

Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes Fogo, Raymond Harris, Mark de Caestecker, Mattias Heinrich, Jeffery Spraggins, Yuankai Huo, Bennett Landman

Proceedings Volume 11596, 115961T (2021) https://doi.org/10.1117/12.2580561

KEYWORDS: Kidney, Computed tomography, Information visualization, Visualization, Tissues, Associative arrays, Abdomen

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Proceedings Article | 15 February 2021 Presentation + Paper

Renal cortex, medulla and pelvicaliceal system segmentation on arterial phase CT images with random patch-based networks

Yucheng Tang, Riqiang Gao, Ho Hin Lee, Zhoubing Xu, Brent Savoie, Shunxing Bao, Yuankai Huo, Agnes Fogo, Raymond Harris, Mark de Caestecker, Jeffery Spraggins, Bennett Landman

Proceedings Volume 11596, 115961D (2021) https://doi.org/10.1117/12.2581101

KEYWORDS: Image segmentation, Computed tomography, Kidney, Neural networks

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Proceedings Article | 10 March 2020 Presentation + Paper

Contrast phase classification with a generative adversarial network

Yucheng Tang, Ho Hin Lee, Yuchen Xu, Olivia Tang, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Camilo Bermudez, Michael Savona, Richard Abramson, Yuankai Huo, Bennett Landman

Proceedings Volume 11313, 1131310 (2020) https://doi.org/10.1117/12.2549438

KEYWORDS: Image classification, Computed tomography, Gallium nitride, Image enhancement, Image contrast enhancement, Tissues, Heart, Kidney, Urinary system, Computer programming

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Dynamic contrast enhanced computed tomography (CT) is an imaging technique that provides critical information on the relationship of vascular structure and dynamics in the context of underlying anatomy. A key challenge for image processing with contrast enhanced CT is that phase discrepancies are latent in different tissues due to contrast protocols, vascular dynamics, and metabolism variance. Previous studies with deep learning frameworks have been proposed for classifying contrast enhancement with networks inspired by computer vision. Here, we revisit the challenge in the context of whole abdomen contrast enhanced CTs. To capture and compensate for the complex contrast changes, we propose a novel discriminator in the form of a multi-domain disentangled representation learning network. The goal of this network is to learn an intermediate representation that separates contrast enhancement from anatomy and enables classification of images with varying contrast time. Briefly, our unpaired contrast disentangling GAN(CD-GAN) Discriminator follows the ResNet architecture to classify a CT scan from different enhancement phases. To evaluate the approach, we trained the enhancement phase classifier on 21060 slices from two clinical cohorts of 230 subjects. The scans were manually labeled with three independent enhancement phases (non-contrast, portal venous and delayed). Testing was performed on 9100 slices from 30 independent subjects who had been imaged with CT scans from all contrast phases. Performance was quantified in terms of the multi-class normalized confusion matrix. The proposed network significantly improved correspondence over baseline UNet, ResNet50 and StarGAN’s performance of accuracy scores 0.54. 0.55, 0.62 and 0.91, respectively (p-value<0.0001 paired t-test for ResNet versus CD-GAN). The proposed discriminator from the disentangled network presents a promising technique that may allow deeper modeling of dynamic imaging against patient specific anatomies.

Proceedings Article | 10 March 2020 Paper

Outlier guided optimization of abdominal segmentation

Yuchen Xu, Olivia Tang, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael Savona, Richard Abramson, Yuankai Huo, Bennett Landman

Proceedings Volume 11313, 1131336 (2020) https://doi.org/10.1117/12.2549365

KEYWORDS: Data modeling, Image segmentation, Pancreas, 3D modeling, Image processing algorithms and systems, Computed tomography, Medical imaging, Abdomen, Visualization, Spleen

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Proceedings Article | 10 March 2020 Presentation + Paper

Semi-supervised multi-organ segmentation through quality assurance supervision

Ho Hin Lee, Yucheng Tang, Olivia Tang, Yuchen Xu, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael Savona, Richard Abramson, Yuankai Huo, Bennett Landman

Proceedings Volume 11313, 113131I (2020) https://doi.org/10.1117/12.2549033

KEYWORDS: Image segmentation, Image quality, 3D modeling, Medical imaging, Data modeling, 3D image processing, Performance modeling, Image processing, Kidney, Spleen

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Human in-the-loop quality assurance (QA) is typically performed after medical image segmentation to ensure that the systems are performing as intended, as well as identifying and excluding outliers. By performing QA on large-scale, previously unlabeled testing data, categorical QA scores (e.g. “successful” versus “unsuccessful”) can be generated. Unfortunately, the precious use of resources for human in-the-loop QA scores are not typically reused in medical image machine learning, especially to train a deep neural network for image segmentation. Herein, we perform a pilot study to investigate if the QA labels can be used as supplementary supervision to augment the training process in a semi-supervised fashion. In this paper, we propose a semi-supervised multi-organ segmentation deep neural network consisting of a traditional segmentation model generator and a QA involved discriminator. An existing 3-D abdominal segmentation network is employed, while the pre-trained ResNet-18 network is used as discriminator. A large-scale dataset of 2027 volumes are used to train the generator, whose 2-D montage images and segmentation mask with QA scores are used to train the discriminator. To generate the QA scores, the 2-D montage images were reviewed manually and coded 0 (success), 1 (errors consistent with published performance), and 2 (gross failure). Then, the ResNet-18 network was trained with 1623 montage images in equal distribution of all three code labels and achieved an accuracy 94% for classification predictions with 404 montage images withheld for the test cohort. To assess the performance of using the QA supervision, the discriminator was used as a loss function in a multi-organ segmentation pipeline. The inclusion of QA-loss function boosted performance on the unlabeled test dataset from 714 patients to 951 patients over the baseline model. Additionally, the number of failures decreased from 606 (29.90%) to 402 (19.83%). The contributions of the proposed method are threefold: We show that (1) the QA scores can be used as a loss function to perform semi-supervised learning for unlabeled data, (2) the well trained discriminator is learnt by QA score rather than traditional “true/false”, and (3) the performance of multi-organ segmentation on unlabeled datasets can be fine-tuned with more robust and higher accuracy than the original baseline method. The use of QA-inspired loss functions represents a promising area of future research and may permit tighter integration of supervised and semi-supervised learning.

Proceedings Article | 10 March 2020 Paper

Validation and optimization of multi-organ segmentation on clinical imaging archives

Olivia Tang, Yuchen Xu, Yucheng Tang, Ho Hin Lee, Yunqiang Chen, Dashan Gao, Shizhong Han, Riqiang Gao, Michael Savona, Richard Abramson, Yuankai Huo, Bennett Landman

Proceedings Volume 11313, 113132O (2020) https://doi.org/10.1117/12.2549035

KEYWORDS: Image segmentation, Liver, Gallbladder, 3D modeling, Computed tomography, Spleen, Data modeling, 3D image processing, Contamination, Abdomen

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