Dr. Bofan Song Profile

Dr. Bofan Song

Associate Research Professor at Wyant College of Optical Sciences

SPIE Involvement:

Conference Program Committee | Author

Publications (16)

Proceedings Article | 13 March 2024 Poster

Development of a multimodal intraoral probe for oral cancer

Shaobai Li, Wenjun Kang, Yihan Wang, Hongzhang Ma, Bofan Song, Rongguang Liang

Proceedings Volume PC12834, PC128340S (2024) https://doi.org/10.1117/12.3007491

KEYWORDS: Cancer, Optical coherence tomography, Cancer detection, Autofluorescence, Visualization, Tissues, Scattering, Prototyping, Medicine, In vivo imaging

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Proceedings Article | 13 March 2024 Presentation

Widefield multispectral imaging to evaluate spectral biomarkers associated with oral epithelial dysplasia and squamous cell carcinoma in a hamster buccal model and human clinical samples

Paula Villarreal, Suimin Qiu, Orly Coblens, Tyler Peterson, Bofan Song, Xingde Li, Rongguang Liang, Gracie Vargas

Proceedings Volume PC12818, (2024) https://doi.org/10.1117/12.3003202

KEYWORDS: Multispectral imaging, Tumor growth modeling, Statistical modeling, Cancer detection, Tissues, Autofluorescence, Tunable filters, Neck, Liquid crystals, Light emitting diodes

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Proceedings Article | 13 March 2024 Presentation

Reliable and trustworthy deep learning algorithm design for oral cancer image analysis

Bofan Song, Dharma KC, Shaobai Li, Chicheng Zhang, Rongguang Liang

Proceedings Volume PC12833, PC1283302 (2024) https://doi.org/10.1117/12.3007296

KEYWORDS: Deep learning, Cancer, Artificial intelligence, Image analysis, Evolutionary algorithms, Design and modelling, Visualization, Visual process modeling, Reliability, Systems modeling

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Proceedings Article | 13 March 2024 Presentation

Benchtop to handheld: characterization and optimization of a ring illuminator/detector in a handheld endogenous fluorescence imaging probe

Paula Villarreal, Tyler Peterson, Bofan Song, Rongguang Liang, Gracie Vargas

Proceedings Volume PC12833, PC1283304 (2024) https://doi.org/10.1117/12.3009704

KEYWORDS: Fluorescence, Light sources and illumination, Imaging systems, Autofluorescence imaging, Signal detection, Light emitting diodes, LED lighting, Fluorescence imaging, Diseases and disorders, Ultraviolet light emitting diodes

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Proceedings Article | 13 March 2024 Presentation + Paper

Preclinical evaluation of a handheld theranostic intraoral device for image-guided PDT treatment of pre-malignant and malignant oral lesions

Shakir Khan, Bofan Song, Sergio Farias, Rongguang Liang, Tayyaba Hasan, Jonathan Celli

Proceedings Volume 12823, 1282302 (2024) https://doi.org/10.1117/12.3001825

KEYWORDS: Photodynamic therapy, Tumors, Fluorescence, Tissues, Photobleaching, Fluorescence imaging, Imaging devices, Cancer, Cameras, Mouse models

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Significance: Oral squamous cell carcinoma (OSCC) has an exceptionally high rate of incidence and mortality in India, with 15 cases per 100,000 people and over 70,000 deaths annually. The problem is exacerbated due to insufficient medical infrastructure for widescale screening and oncology care, particularly in rural regions. New technologies are urgently needed to detect oral cancer and provide timely treatment at the point of care. This work draws upon previous development and clinical validation of low-cost hardware for photodynamic therapy (PDT) treatment of oral lesions combined with an intraoral probe for cancer screening, incorporated here into an integrated theranostic device for image-guided PDT. Aim: This study aimed to validate technical performance of a novel hand-held smartphone-coupled intraoral device designed for simultaneous imaging and photodynamic therapy (PDT) of oral lesions. The imaging and PDT light delivery capabilities of the handheld system were evaluated using tissue phantoms containing protoporphyrin IX (PpIX) and a mouse model of OSCC photosensitized using 5-aminolevulinic acid (ALA)-induced PpIX. Approach: The probe’s built-in multi-channel fluorescence and polarized white light imaging capabilities were evaluated using tissue phantoms with TiO₂ and controlled PpIX concentrations. In-vivo testing was performed using mice with subcutaneous TR146 OSCC implants before and after administering ALA, and again to assess photobleaching after light delivery (a total of 100J/cm² at 635nm from the integrated diode laser). Results: Quantification of fluorescence images generated using the device showed that the PpIX signal scaled linearly with concentration, and the extent of photobleaching increased with increasing PDT dose as expected. In murine xenografts, PDT treatment delivered through the intraoral probe reduced tumor volume significantly in comparison to untreated control animals. Conclusion: Our findings demonstrate the effectiveness of a low-cost handheld device for simultaneous quantitative imaging of PpIX fluorescence and image-guided PDT in vivo. The integration of intraoral imaging and image-guided treatment into the same handheld device paves the way for a streamlined approach to cancer screening and early intervention with PDT at the point of care.

SPIE Journal Paper | 21 July 2023 Open Access

Clinical assessment of a low-cost, hand-held, smartphone-attached intraoral imaging probe for 5-aminolevulinic acid photodynamic therapy monitoring and guidance

Shakir Khan, Bofan Song, Srivalleesha Mallidi, Shaobai Li, Hui Liu, M. A. Bilal Hussain, Shaista Siddiqui, Amjad P. Khan, Kafil Akhtar, Shahid Ali Siddiqui, Syed Abrar Hasan, Colin Hopper, Stephen G. Bown, Rongguang Liang, Tayyaba Hasan, Jonathan P. Celli

JBO, Vol. 28, Issue 08, 082809, (July 2023) https://doi.org/10.1117/12.10.1117/1.JBO.28.8.082809

KEYWORDS: Photodynamic therapy, Tissues, Image segmentation, Cancer, Oncology, Biomedical optics, Tumors, Imaging systems, Light emitting diodes, Collagen

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SignificanceIndia has one of the highest rates of oral squamous cell carcinoma (OSCC) in the world, with an incidence of 15 per 100,000 and more than 70,000 deaths per year. The problem is exacerbated by a lack of medical infrastructure and routine screening, especially in rural areas. New technologies for oral cancer detection and timely treatment at the point of care are urgently needed.AimOur study aimed to use a hand-held smartphone-coupled intraoral imaging device, previously investigated for autofluorescence (auto-FL) diagnostics adapted here for treatment guidance and monitoring photodynamic therapy (PDT) using 5-aminolevulinic acid (ALA)-induced protoporphyrin IX (PpIX) fluorescence (FL).ApproachA total of 12 patients with 14 buccal mucosal lesions having moderately/well-differentiated micro-invasive OSCC lesions (<2 cm diameter and <5 mm depth) were systemically (in oral solution) administered three doses of 20 mg / kg ALA (total 60 mg / kg). Lesion site PpIX and auto-FL were imaged using the multichannel FL and polarized white-light oral cancer imaging probe before/after ALA administration and after light delivery (fractionated, total 100 J / cm2 of 635 nm red LED light).ResultsThe handheld device was conducive for access to lesion site images in the oral cavity. Segmentation of ratiometric images in which PpIX FL is mapped relative to auto-FL enabled improved demarcation of lesion boundaries relative to PpIX alone. A relative FL (R-value) threshold of 1.4 was found to segment lesion site PpIX production among the patients with mild to severe dysplasia malignancy. The segmented lesion size is well correlated with ultrasound findings. Lesions for which R-value was >1.65 at the time of treatment were associated with successful outcomes.ConclusionThese results indicate the utility of a low-cost, handheld intraoral imaging probe for image-guided PDT and treatment monitoring while also laying the groundwork for an integrated approach, combining cancer screening and treatment with the same hardware.

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SPIE Journal Paper | 3 November 2022 Open Access

Exploring uncertainty measures in convolutional neural network for semantic segmentation of oral cancer images

Bofan Song, Shaobai Li, Sumsum Sunny, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Tyler Peterson, Shubha Gurudath, Subhashini Raghavan, Imchen Tsusennaro, Shirley Leivon, Trupti Kolur, Vivek Shetty, Vidya Bushan, Rohan Ramesh, Vijay Pillai, Petra Wilder-Smith, Amritha Suresh, Moni Abraham Kuriakose, Praveen Birur, Rongguang Liang

JBO, Vol. 27, Issue 11, 115001, (November 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.11.115001

KEYWORDS: Image segmentation, Cancer, Tumor growth modeling, Performance modeling, Convolution, Instrument modeling, Reliability, Medical research, Inspection, Skin cancer

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SignificanceOral cancer is one of the most prevalent cancers, especially in middle- and low-income countries such as India. Automatic segmentation of oral cancer images can improve the diagnostic workflow, which is a significant task in oral cancer image analysis. Despite the remarkable success of deep-learning networks in medical segmentation, they rarely provide uncertainty quantification for their output.AimWe aim to estimate uncertainty in a deep-learning approach to semantic segmentation of oral cancer images and to improve the accuracy and reliability of predictions.ApproachThis work introduced a UNet-based Bayesian deep-learning (BDL) model to segment potentially malignant and malignant lesion areas in the oral cavity. The model can quantify uncertainty in predictions. We also developed an efficient model that increased the inference speed, which is almost six times smaller and two times faster (inference speed) than the original UNet. The dataset in this study was collected using our customized screening platform and was annotated by oral oncology specialists.ResultsThe proposed approach achieved good segmentation performance as well as good uncertainty estimation performance. In the experiments, we observed an improvement in pixel accuracy and mean intersection over union by removing uncertain pixels. This result reflects that the model provided less accurate predictions in uncertain areas that may need more attention and further inspection. The experiments also showed that with some performance compromises, the efficient model reduced computation time and model size, which expands the potential for implementation on portable devices used in resource-limited settings.ConclusionsOur study demonstrates the UNet-based BDL model not only can perform potentially malignant and malignant oral lesion segmentation, but also can provide informative pixel-level uncertainty estimation. With this extra uncertainty information, the accuracy and reliability of the model’s prediction can be improved.

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Proceedings Article | 4 March 2022 Presentation + Paper

Clinical assessment of a low-cost, hand-held, smartphone-attached intraoral imaging probe for ALA PDT monitoring and guidance

Shakir Khan, Bofan Song, Srivalleesha Mallidi, Shaobai Li, Hui Liu, M. A. Bilal Hussain, Mayra BaptistaLopesTeix, Shaista Siddiqui, Amjad Khan, Kafil Akhtar, Shahid Ali Siddiqui, Syed Abrar Hasan, Colin Hopper, Stephen Bown, Rongguang Liang, Tayyaba Hasan, Jonathan Celli

Proceedings Volume 11940, 1194004 (2022) https://doi.org/10.1117/12.2609956

KEYWORDS: Photodynamic therapy, Cancer, Image segmentation, Tissues, Oncology, Light emitting diodes, Imaging systems, Visualization

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SPIE Journal Paper | 12 January 2022 Open Access

Interpretable deep learning approach for oral cancer classification using guided attention inference network

Kevin Figueroa, Bofan Song, Sumsum Sunny, Shaobai Li, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Shubha Gurudath, Subhashini Raghavan, Imchen Tsusennaro, Shirley Leivon, Trupti Kolur, Vivek Shetty, Vidya Bushan, Rohan Ramesh, Vijay Pillai, Petra Wilder-Smith, Alben Sigamani, Amritha Suresh, Moni Kuriakose, Praveen Birur, Rongguang Liang

JBO, Vol. 27, Issue 01, 015001, (January 2022) https://doi.org/10.1117/12.10.1117/1.JBO.27.1.015001

KEYWORDS: Cancer, Image classification, Network architectures, Visualization, Mining, Lithium, Tongue, Image segmentation, Image processing, Teeth

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Significance: Convolutional neural networks (CNNs) show the potential for automated classification of different cancer lesions. However, their lack of interpretability and explainability makes CNNs less than understandable. Furthermore, CNNs may incorrectly concentrate on other areas surrounding the salient object, rather than the network’s attention focusing directly on the object to be recognized, as the network has no incentive to focus solely on the correct subjects to be detected. This inhibits the reliability of CNNs, especially for biomedical applications. Aim: Develop a deep learning training approach that could provide understandability to its predictions and directly guide the network to concentrate its attention and accurately delineate cancerous regions of the image. Approach: We utilized Selvaraju et al.’s gradient-weighted class activation mapping to inject interpretability and explainability into CNNs. We adopted a two-stage training process with data augmentation techniques and Li et al.’s guided attention inference network (GAIN) to train images captured using our customized mobile oral screening devices. The GAIN architecture consists of three streams of network training: classification stream, attention mining stream, and bounding box stream. By adopting the GAIN training architecture, we jointly optimized the classification and segmentation accuracy of our CNN by treating these attention maps as reliable priors to develop attention maps with more complete and accurate segmentation. Results: The network’s attention map will help us to actively understand what the network is focusing on and looking at during its decision-making process. The results also show that the proposed method could guide the trained neural network to highlight and focus its attention on the correct lesion areas in the images when making a decision, rather than focusing its attention on relevant yet incorrect regions. Conclusions: We demonstrate the effectiveness of our approach for more interpretable and reliable oral potentially malignant lesion and malignant lesion classification.

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SPIE Journal Paper | 23 October 2021 Open Access

Classification of imbalanced oral cancer image data from high-risk population

Bofan Song, Shaobai Li, Sumsum Sunny, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Shubha Gurudath, Subhashini Raghavan, Imchen Tsusennaro, Shirley Leivon, Trupti Kolur, Vivek Shetty, Vidya Bushan, Rohan Ramesh, Tyler Peterson, Vijay Pillai, Petra Wilder-Smith, Alben Sigamani, Amritha Suresh, Moni Kuriakose, Praveen Birur, Rongguang Liang

JBO, Vol. 26, Issue 10, 105001, (October 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.10.105001

KEYWORDS: Cancer, Image classification, Data modeling, Tumor growth modeling, Medical imaging, Neural networks, Medical research, Breast cancer, Biomedical optics, Mobile devices

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SPIE Journal Paper | 23 June 2021 Open Access

Mobile-based oral cancer classification for point-of-care screening

Bofan Song, Sumsum Sunny, Shaobai Li, Keerthi Gurushanth, Pramila Mendonca, Nirza Mukhia, Sanjana Patrick, Shubha Gurudath, Subhashini Raghavan, Tsusennaro Imchen, Shirley Leivon, Trupti Kolur, Vivek Shetty, Vidya Bushan, Rohan Ramesh, Natzem Lima, Vijay Pillai, Petra Wilder-Smith, Alben Sigamani, Amritha Suresh, Moni Kuriakose, Praveen Birur, Rongguang Liang

JBO, Vol. 26, Issue 06, 065003, (June 2021) https://doi.org/10.1117/12.10.1117/1.JBO.26.6.065003

KEYWORDS: Cancer, Image classification, Point-of-care devices, Internet, Convolution, Optimization (mathematics), Image processing, Tumor growth modeling, Light emitting diodes, Gold

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SPIE Journal Paper | 23 June 2020 Open Access

Point-of-care, multispectral, smartphone-based dermascopes for dermal lesion screening and erythema monitoring

Ross Uthoff, Bofan Song, Melody Maarouf, Vivian Shi, Rongguang Liang

JBO, Vol. 25, Issue 06, 066004, (June 2020) https://doi.org/10.1117/12.10.1117/1.JBO.25.6.066004

KEYWORDS: Cameras, Chromophores, Light emitting diodes, Imaging systems, Point-of-care devices, Modulation transfer functions, Image processing, RGB color model, Melanoma, LED lighting

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Proceedings Article | 9 March 2020 Presentation

Dual-modal oral cancer screening platform and automatic classification algorithm for low-resource settings (Conference Presentation)

Bofan Song, Sumsum Sunny, Ross Uthoff, Sanjana Patrick, Amritha Suresh, Trupti Kolur, G. Keerthi, Afarin Anbarani, Petra Wilder-Smith, Moni Kuriakose, Praveen Birur, Jeffrey Rodriguez, Rongguang Liang

Proceedings Volume 11230, 112300V (2020) https://doi.org/10.1117/12.2552213

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SPIE Journal Paper | 22 October 2019 Open Access

Small form factor, flexible, dual-modality handheld probe for smartphone-based, point-of-care oral and oropharyngeal cancer screening

Ross Uthoff, Bofan Song, Sumsum Sunny, Sanjana Patrick, Amritha Suresh, Trupti Kolur, Keerthi Gurushanth, Kimberly Wooten, Vishal Gupta, Mary Platek, Anurag Singh, Petra Wilder-Smith, Moni Abraham Kuriakose, Praveen Birur, Rongguang Liang

JBO, Vol. 24, Issue 10, 106003, (October 2019) https://doi.org/10.1117/12.10.1117/1.JBO.24.10.106003

KEYWORDS: Cameras, Light emitting diodes, Cancer, Silicon, Imaging systems, Head, Modulation transfer functions, Point-of-care devices, Biomedical optics, Optical filters

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Proceedings Article | 19 February 2018 Presentation + Paper

Development of a dual-modality, dual-view smartphone-based imaging system for oral cancer detection

Ross Uthoff, Bofan Song, Praveen Birur, Moni Abraham Kuriakose, Sumsum Sunny, Amritha Suresh, Sanjana Patrick, Afarin Anbarani, Oliver Spires, Petra Wilder-Smith, Rongguang Liang

Proceedings Volume 10486, 104860V (2018) https://doi.org/10.1117/12.2296435

KEYWORDS: Imaging systems, Cancer, Cameras, Mouth, Light emitting diodes, Image processing, Biomedical optics, Auto-fluorescence imaging, Mobile device imaging

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SPIE Journal Paper | 3 November 2014 Open Access

In vivo near-infrared fluorescence three-dimensional positioning system with binocular stereovision

Bofan Song, Wei Jin, Ying Wang, Qinhan Jin, Ying Mu

JBO, Vol. 19, Issue 11, 116002, (November 2014) https://doi.org/10.1117/12.10.1117/1.JBO.19.11.116002

KEYWORDS: Luminescence, In vivo imaging, 3D acquisition, Imaging systems, Cameras, 3D image processing, Signal detection, Multispectral imaging, Quantum dots, Charge-coupled devices

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Showing 5 of 16 publications

Conference Committee Involvement (2)

Design and Quality for Biomedical Technologies XVIII

25 January 2025 | San Francisco, California, United States

Design and Quality for Biomedical Technologies XVII

27 January 2024 | San Francisco, California, United States

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