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  1. Book ; Online: Automated Segmentation of Computed Tomography Images with Submanifold Sparse Convolutional Networks

    Alonso-Monsalve, Saúl / Whitehead, Leigh H. / Aurisano, Adam / Sanchez, Lorena Escudero

    2022  

    Abstract: Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of tumours in medical imaging have been extensively developed in recent ... ...

    Abstract Quantitative cancer image analysis relies on the accurate delineation of tumours, a very specialised and time-consuming task. For this reason, methods for automated segmentation of tumours in medical imaging have been extensively developed in recent years, being Computed Tomography one of the most popular imaging modalities explored. However, the large amount of 3D voxels in a typical scan is prohibitive for the entire volume to be analysed at once in conventional hardware. To overcome this issue, the processes of downsampling and/or resampling are generally implemented when using traditional convolutional neural networks in medical imaging. In this paper, we propose a new methodology that introduces a process of sparsification of the input images and submanifold sparse convolutional networks as an alternative to downsampling. As a proof of concept, we applied this new methodology to Computed Tomography images of renal cancer patients, obtaining performances of segmentations of kidneys and tumours competitive with previous methods (~84.6% Dice similarity coefficient), while achieving a significant improvement in computation time (2-3 min per training epoch).
    Keywords Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition
    Subject code 004
    Publishing date 2022-12-06
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Reproducibility of CT-based radiomic features against image resampling and perturbations for tumour and healthy kidney in renal cancer patients.

    Mottola, Margherita / Ursprung, Stephan / Rundo, Leonardo / Sanchez, Lorena Escudero / Klatte, Tobias / Mendichovszky, Iosif / Stewart, Grant D / Sala, Evis / Bevilacqua, Alessandro

    Scientific reports

    2021  Volume 11, Issue 1, Page(s) 11542

    Abstract: Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the ... ...

    Abstract Computed Tomography (CT) is widely used in oncology for morphological evaluation and diagnosis, commonly through visual assessments, often exploiting semi-automatic tools as well. Well-established automatic methods for quantitative imaging offer the opportunity to enrich the radiologist interpretation with a large number of radiomic features, which need to be highly reproducible to be used reliably in clinical practice. This study investigates feature reproducibility against noise, varying resolutions and segmentations (achieved by perturbing the regions of interest), in a CT dataset with heterogeneous voxel size of 98 renal cell carcinomas (RCCs) and 93 contralateral normal kidneys (CK). In particular, first order (FO) and second order texture features based on both 2D and 3D grey level co-occurrence matrices (GLCMs) were considered. Moreover, this study carries out a comparative analysis of three of the most commonly used interpolation methods, which need to be selected before any resampling procedure. Results showed that the Lanczos interpolation is the most effective at preserving original information in resampling, where the median slice resolution coupled with the native slice spacing allows the best reproducibility, with 94.6% and 87.7% of features, in RCC and CK, respectively. GLCMs show their maximum reproducibility when used at short distances.
    MeSH term(s) Algorithms ; Carcinoma, Renal Cell/diagnostic imaging ; Humans ; Image Processing, Computer-Assisted/methods ; Kidney/diagnostic imaging ; Kidney Neoplasms/diagnostic imaging ; Reproducibility of Results ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2021-06-02
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-021-90985-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies.

    Doran, Simon J / Al Sa'd, Mohammad / Petts, James A / Darcy, James / Alpert, Kate / Cho, Woonchan / Sanchez, Lorena Escudero / Alle, Sachidanand / El Harouni, Ahmed / Genereaux, Brad / Ziegler, Erik / Harris, Gordon J / Aboagye, Eric O / Sala, Evis / Koh, Dow-Mu / Marcus, Dan

    Tomography (Ann Arbor, Mich.)

    2022  Volume 8, Issue 1, Page(s) 497–512

    Abstract: ... ...

    Abstract Purpose
    MeSH term(s) Archives ; Artificial Intelligence ; Diagnostic Imaging ; Humans ; Software
    Language English
    Publishing date 2022-02-11
    Publishing country Switzerland
    Document type Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
    ISSN 2379-139X
    ISSN (online) 2379-139X
    DOI 10.3390/tomography8010040
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Book ; Online: Advancing COVID-19 Diagnosis with Privacy-Preserving Collaboration in Artificial Intelligence

    Bai, Xiang / Wang, Hanchen / Ma, Liya / Xu, Yongchao / Gan, Jiefeng / Fan, Ziwei / Yang, Fan / Ma, Ke / Yang, Jiehua / Bai, Song / Shu, Chang / Zou, Xinyu / Huang, Renhao / Zhang, Changzheng / Liu, Xiaowu / Tu, Dandan / Xu, Chuou / Zhang, Wenqing / Wang, Xi /
    Chen, Anguo / Zeng, Yu / Yang, Dehua / Wang, Ming-Wei / Holalkere, Nagaraj / Halin, Neil J. / Kamel, Ihab R. / Wu, Jia / Peng, Xuehua / Wang, Xiang / Shao, Jianbo / Mongkolwat, Pattanasak / Zhang, Jianjun / Liu, Weiyang / Roberts, Michael / Teng, Zhongzhao / Beer, Lucian / Sanchez, Lorena Escudero / Sala, Evis / Rubin, Daniel / Weller, Adrian / Lasenby, Joan / Zheng, Chuangsheng / Wang, Jianming / Li, Zhen / Schönlieb, Carola-Bibiane / Xia, Tian

    2021  

    Abstract: Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable ... ...

    Abstract Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

    Comment: Nature Machine Intelligence
    Keywords Computer Science - Artificial Intelligence ; Computer Science - Cryptography and Security
    Subject code 006
    Publishing date 2021-11-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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