Ms. Hongshan Liu Profile

Hongshan Liu

at Stevens Institute of Technology

SPIE Involvement:

Author

Publications (5)

Proceedings Article | 2 April 2024 Presentation + Paper

Label refinement for noisy annotation in weakly supervised segmentation

Ziyi Huang, Hongshan Liu, Haofeng Zhang, Fuyong Xing, Andrew Laine, Elsa Angelini, Christine Hendon, Yu Gan

Proceedings Volume 12926, 129260W (2024) https://doi.org/10.1117/12.3006817

KEYWORDS: Image segmentation, Neural networks, Optical coherence tomography, Medical imaging, Deep learning

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SPIE Journal Paper | 25 March 2024 Open Access

Structurally constrained and pathology-aware convolutional transformer generative adversarial network for virtual histology staining of human coronary optical coherence tomography images

Xueshen Li, Hongshan Liu, Xiaoyu Song, Charles Marboe, Brigitta Brott, Silvio Litovsky, Yu Gan

JBO, Vol. 29, Issue 03, 036004, (March 2024) https://doi.org/10.1117/12.10.1117/1.JBO.29.3.036004

KEYWORDS: Optical coherence tomography, Transformers, Pathology, Education and training, Design, Histopathology, Arteries, Visualization, Image quality, Gallium nitride

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SignificanceThere is a significant need for the generation of virtual histological information from coronary optical coherence tomography (OCT) images to better guide the treatment of coronary artery disease (CAD). However, existing methods either require a large pixel-wise paired training dataset or have limited capability to map pathological regions.AimThe aim of this work is to generate virtual histological information from coronary OCT images, without a pixel-wise paired training dataset while capable of providing pathological patterns.ApproachWe design a structurally constrained, pathology-aware, transformer generative adversarial network, namely structurally constrained pathology-aware convolutional transformer generative adversarial network (SCPAT-GAN), to generate virtual stained H&E histology from OCT images. We quantitatively evaluate the quality of virtual stained histology images by measuring the Fréchet inception distance (FID) and perceptual hash value (PHV). Moreover, we invite experienced pathologists to evaluate the virtual stained images. Furthermore, we visually inspect the virtual stained image generated by SCPAT-GAN. Also, we perform an ablation study to validate the design of the proposed SCPAT-GAN. Finally, we demonstrate 3D virtual stained histology images.ResultsCompared to previous research, the proposed SCPAT-GAN achieves better FID and PHV scores. The visual inspection suggests that the virtual histology images generated by SCPAT-GAN resemble both normal and pathological features without artifacts. As confirmed by the pathologists, the virtual stained images have good quality compared to real histology images. The ablation study confirms the effectiveness of the combination of proposed pathological awareness and structural constraining modules.ConclusionsThe proposed SCPAT-GAN is the first to demonstrate the feasibility of generating both normal and pathological patterns without pixel-wisely supervised training. We expect the SCPAT-GAN to assist in the clinical evaluation of treating the CAD by providing 2D and 3D histopathological visualizations.

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SPIE Journal Paper | 27 March 2023 Open Access

Toward reliable calcification detection: calibration of uncertainty in object detection from coronary optical coherence tomography images

Hongshan Liu, Xueshen Li, Abdul Latif Bamba, Xiaoyu Song, Brigitta C. Brott, Silvio H. Litovsky, Yu Gan

JBO, Vol. 28, Issue 03, 036008, (March 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.3.036008

KEYWORDS: Object detection, Calibration, Optical coherence tomography, Deep learning, Image segmentation, Arteries, Data modeling, Electrochemical etching, Computer aided detection, Image processing

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