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  1. Article ; Online: Real-time breast lesion classification combining diffuse optical tomography frequency domain data and BI-RADS assessment.

    Li, Shuying / Zhang, Menghao / Xue, Minghao / Zhu, Quing

    Journal of biophotonics

    2024  Volume 17, Issue 5, Page(s) e202300483

    Abstract: Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image ... ...

    Abstract Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated potential for breast cancer diagnosis, in which real-time or near real-time diagnosis with high accuracy is desired. However, DOT's relatively slow data processing and image reconstruction speeds have hindered real-time diagnosis. Here, we propose a real-time classification scheme that combines US breast imaging reporting and data system (BI-RADS) readings and DOT frequency domain measurements. A convolutional neural network is trained to generate malignancy probability scores from DOT measurements. Subsequently, these scores are integrated with BI-RADS assessments using a support vector machine classifier, which then provides the final diagnostic output. An area under the receiver operating characteristic curve of 0.978 is achieved in distinguishing between benign and malignant breast lesions in patient data without image reconstruction.
    MeSH term(s) Humans ; Tomography, Optical/methods ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/pathology ; Female ; Image Processing, Computer-Assisted/methods ; Time Factors ; Neural Networks, Computer
    Language English
    Publishing date 2024-03-02
    Publishing country Germany
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2390063-5
    ISSN 1864-0648 ; 1864-063X
    ISSN (online) 1864-0648
    ISSN 1864-063X
    DOI 10.1002/jbio.202300483
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 6723: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

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  2. Article ; Online: Two-stage classification strategy for breast cancer diagnosis using ultrasound-guided diffuse optical tomography and deep learning.

    Zhang, Menghao / Li, Shuying / Xue, Minghao / Zhu, Quing

    Journal of biomedical optics

    2023  Volume 28, Issue 8, Page(s) 86002

    Abstract: Significance: Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.: Aim: We aim to use US-guided DOT to ... ...

    Abstract Significance: Ultrasound (US)-guided diffuse optical tomography (DOT) has demonstrated great potential for breast cancer diagnosis in which real-time or near real-time diagnosis with high accuracy is desired.
    Aim: We aim to use US-guided DOT to achieve an automated, fast, and accurate classification of breast lesions.
    Approach: We propose a two-stage classification strategy with deep learning. In the first stage, US images and histograms created from DOT perturbation measurements are combined to predict benign lesions. Then the non-benign suspicious lesions are passed through to the second stage, which combine US image features, DOT histogram features, and 3D DOT reconstructed images for final diagnosis.
    Results: The first stage alone identified 73.0% of benign cases without image reconstruction. In distinguishing between benign and malignant breast lesions in patient data, the two-stage classification approach achieved an area under the receiver operating characteristic curve of 0.946, outperforming the diagnoses of all single-modality models and of a single-stage classification model that combines all US images, DOT histogram, and imaging features.
    Conclusions: The proposed two-stage classification strategy achieves better classification accuracy than single-modality-only models and a single-stage classification model that combines all features. It can potentially distinguish breast cancers from benign lesions in near real-time.
    MeSH term(s) Humans ; Female ; Breast Neoplasms/diagnostic imaging ; Deep Learning ; Breast/diagnostic imaging ; Tomography, Optical ; Ultrasonography, Interventional
    Language English
    Publishing date 2023-08-26
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1309154-2
    ISSN 1560-2281 ; 1083-3668
    ISSN (online) 1560-2281
    ISSN 1083-3668
    DOI 10.1117/1.JBO.28.8.086002
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4699: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  3. Article: Fusion deep learning approach combining diffuse optical tomography and ultrasound for improving breast cancer classification.

    Zhang, Menghao / Xue, Minghao / Li, Shuying / Zou, Yun / Zhu, Quing

    Biomedical optics express

    2023  Volume 14, Issue 4, Page(s) 1636–1646

    Abstract: Diffuse optical tomography (DOT) is a promising technique that provides functional information related to tumor angiogenesis. However, reconstructing the DOT function map of a breast lesion is an ill-posed and underdetermined inverse process. A co- ... ...

    Abstract Diffuse optical tomography (DOT) is a promising technique that provides functional information related to tumor angiogenesis. However, reconstructing the DOT function map of a breast lesion is an ill-posed and underdetermined inverse process. A co-registered ultrasound (US) system that provides structural information about the breast lesion can improve the localization and accuracy of DOT reconstruction. Additionally, the well-known US characteristics of benign and malignant breast lesions can further improve cancer diagnosis based on DOT alone. Inspired by a fusion model deep learning approach, we combined US features extracted by a modified VGG-11 network with images reconstructed from a DOT deep learning auto-encoder-based model to form a new neural network for breast cancer diagnosis. The combined neural network model was trained with simulation data and fine-tuned with clinical data: it achieved an AUC of 0.931 (95% CI: 0.919-0.943), superior to those achieved using US images alone (0.860) or DOT images alone (0.842).
    Language English
    Publishing date 2023-03-27
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2572216-5
    ISSN 2156-7085
    ISSN 2156-7085
    DOI 10.1364/BOE.486292
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article: Automated pipeline for breast cancer diagnosis using US assisted diffuse optical tomography.

    Xue, Minghao / Zhang, Menghao / Li, Shuying / Zou, Yun / Zhu, Quing

    Biomedical optics express

    2023  Volume 14, Issue 11, Page(s) 6072–6087

    Abstract: Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor ... ...

    Abstract Ultrasound (US)-guided diffuse optical tomography (DOT) is a portable and non-invasive imaging modality for breast cancer diagnosis and treatment response monitoring. However, DOT data pre-processing and imaging reconstruction often require labor intensive manual processing which hampers real-time diagnosis. In this study, we aim at providing an automated US-assisted DOT pre-processing, imaging and diagnosis pipeline to achieve near real-time diagnosis. We have developed an automated DOT pre-processing method including motion detection, mismatch classification using deep-learning approach, and outlier removal. US-lesion information needed for DOT reconstruction was extracted by a semi-automated lesion segmentation approach combined with a US reading algorithm. A deep learning model was used to evaluate the quality of the reconstructed DOT images and a two-step deep-learning model developed earlier is implemented to provide final diagnosis based on US imaging features and DOT measurements and imaging results. The presented US-assisted DOT pipeline accurately processed the DOT measurements and reconstruction and reduced the procedure time to 2 to 3 minutes while maintained a comparable classification result with manually processed dataset.
    Language English
    Publishing date 2023-11-01
    Publishing country United States
    Document type Journal Article
    ZDB-ID 2572216-5
    ISSN 2156-7085
    ISSN 2156-7085
    DOI 10.1364/BOE.502244
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.

    Li, Shuying / Zhang, Menghao / Xue, Minghao / Zhu, Quing

    Journal of biomedical optics

    2022  Volume 27, Issue 8

    Abstract: Significance: "Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements ...

    Abstract Significance: "Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measurements is time consuming, and mismatches between the target medium and the reference medium can cause inaccurate reconstruction.
    Aim: We aim to streamline the data acquisition and mitigate the mismatch problems in DOT difference imaging using a deep learning-based approach to generate data from target measurements only.
    Approach: We train an artificial neural network to output data for difference imaging from target measurements only. The model is trained and validated on simulation data and tested with simulations, phantom experiments, and clinical data from 56 patients with breast lesions.
    Results: The proposed method has comparable performance to the traditional approach using measurements without mismatch between the target side and the reference side, and it outperforms the traditional approach using measurements when there is a mismatch. It also improves the target-to-artifact ratio and lesion localization in patient data.
    Conclusions: The proposed method can simplify the data acquisition procedure, mitigate mismatch problems, and improve reconstructed image quality in DOT difference imaging.
    MeSH term(s) Algorithms ; Artifacts ; Deep Learning ; Humans ; Image Processing, Computer-Assisted/methods ; Phantoms, Imaging ; Tomography, Optical/methods
    Language English
    Publishing date 2022-08-25
    Publishing country United States
    Document type Journal Article ; Research Support, N.I.H., Extramural
    ZDB-ID 1309154-2
    ISSN 1560-2281 ; 1083-3668
    ISSN (online) 1560-2281
    ISSN 1083-3668
    DOI 10.1117/1.JBO.27.8.086003
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 4699: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

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  6. Article: Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

    Ghodgaonkar, Isha / Chakraborty, Subhankar / Banna, Vishnu / Allcroft, Shane / Metwaly, Mohammed / Bordwell, Fischer / Kimura, Kohsuke / Zhao, Xinxin / Goel, Abhinav / Tung, Caleb / Chinnakotla, Akhil / Xue, Minghao / Lu, Yung-Hsiang / Ward, Mark Daniel / Zakharov, Wei / Ebert, David S. / Barbarash, David M. / Thiruvathukal, George K.

    Abstract: In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale ( ... ...

    Abstract In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods such as in-person reporting is dangerous because observers may risk infection. A better solution is to observe activities through network cameras; this approach is scalable and observers can stay in safe locations. This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called ''lockdown''). We discover 11,140 network cameras that provide real-time data and we present the results across 15 countries. We collect data from these cameras beginning April 2020 at approximately 0.5TB per week. After analyzing 10,424,459 images from still image cameras and frames extracted periodically from video, the data reveals that the residents in some countries exhibited more activity (judged by numbers of people and vehicles) after the restrictions were lifted. In other countries, the amounts of activities showed no obvious changes during the restrictions and after the restrictions were lifted. The data further reveals whether people stay ''social distancing'', at least 6 feet apart. This study discerns whether social distancing is being followed in several types of locations and geographical locations worldwide and serve as an early indicator whether another wave of infections is likely to occur soon.
    Keywords covid19
    Publisher ArXiv
    Document type Article
    Database COVID19

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  7. Book ; Online: Analyzing Worldwide Social Distancing through Large-Scale Computer Vision

    Ghodgaonkar, Isha / Chakraborty, Subhankar / Banna, Vishnu / Allcroft, Shane / Metwaly, Mohammed / Bordwell, Fischer / Kimura, Kohsuke / Zhao, Xinxin / Goel, Abhinav / Tung, Caleb / Chinnakotla, Akhil / Xue, Minghao / Lu, Yung-Hsiang / Ward, Mark Daniel / Zakharov, Wei / Ebert, David S. / Barbarash, David M. / Thiruvathukal, George K.

    2020  

    Abstract: In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale ( ... ...

    Abstract In order to contain the COVID-19 pandemic, countries around the world have introduced social distancing guidelines as public health interventions to reduce the spread of the disease. However, monitoring the efficacy of these guidelines at a large scale (nationwide or worldwide) is difficult. To make matters worse, traditional observational methods such as in-person reporting is dangerous because observers may risk infection. A better solution is to observe activities through network cameras; this approach is scalable and observers can stay in safe locations. This research team has created methods that can discover thousands of network cameras worldwide, retrieve data from the cameras, analyze the data, and report the sizes of crowds as different countries issued and lifted restrictions (also called ''lockdown''). We discover 11,140 network cameras that provide real-time data and we present the results across 15 countries. We collect data from these cameras beginning April 2020 at approximately 0.5TB per week. After analyzing 10,424,459 images from still image cameras and frames extracted periodically from video, the data reveals that the residents in some countries exhibited more activity (judged by numbers of people and vehicles) after the restrictions were lifted. In other countries, the amounts of activities showed no obvious changes during the restrictions and after the restrictions were lifted. The data further reveals whether people stay ''social distancing'', at least 6 feet apart. This study discerns whether social distancing is being followed in several types of locations and geographical locations worldwide and serve as an early indicator whether another wave of infections is likely to occur soon.

    Comment: 10 pages, 15 figures
    Keywords Computer Science - Computer Vision and Pattern Recognition ; covid19
    Publishing date 2020-08-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

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