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  1. Proceedings of the 20th International Conference on Biomedical Applications of Electrical Impedance Tomography : July 1-3, 2019, UCL, London, UK

    Free access
    International Conference on Biomedical Applications of Electrical Impedance Tomography | TIBKAT | 2019

  2. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels

    Shafiq-ul-Hassan, Muhammad / Zhang, Geoffrey G / Latifi, Kujtim et al. | Online Contents | 2017

  3. MR-based Synthetic CT Generation using a Deep Convolutional Neural Network Method

    Han, Xiao | Online Contents | 2017

  4. Use of image registration and fusion algorithms and techniques in radiotherapy: Report of the AAPM Radiation Therapy Committee Task Group No. 132

    Brock, Kristy K / Mutic, Sasa / McNutt, Todd R et al. | Online Contents | 2017

  5. A deep convolutional neural network using directional wavelets for low‐dose X‐ray CT reconstruction

    Kang, Eunhee / Min, Junhong / Ye, Jong Chul | Online Contents | 2017

  6. Intrinsic dependencies of CT radiomic features on voxel size and number of gray levels

    Shafiq‐ul‐Hassan, Muhammad / Zhang, Geoffrey G / Latifi, Kujtim et al. | Online Contents | 2017

  7. A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets

    Antropova, Natalia / Huynh, Benjamin Q / Giger, Maryellen L | Online Contents | 2017

  8. Segmentation of organs-at-risks in head and neck CT images using convolutional neural networks

    Ibragimov, Bulat / Xing, Lei | Online Contents | 2016

  9. Segmentation of organs‐at‐risks in head and neck CT images using convolutional neural networks

    Ibragimov, Bulat / Xing, Lei | Online Contents | 2017

  10. MR‐based synthetic CT generation using a deep convolutional neural network method

    Han, Xiao | Online Contents | 2017

  11. The report of Task Group 100 of the AAPM: Application of risk analysis methods to radiation therapy quality management

    Huq, M. Saiful / Fraass, Benedick A / Dunscombe, Peter B et al. | Online Contents | 2016

  12. Using deep learning to segment breast and fibroglandular tissue in MRI volumes

    Dalmış, Mehmet Ufuk / Litjens, Geert / Holland, Katharina et al. | Online Contents | 2017

  13. NEMA NU 2-2012 performance studies for the SiPM-based ToF-PET component of the GE SIGNA PET/MR system

    Grant, Alexander M / Deller, Timothy W / Khalighi, Mohammad Mehdi et al. | Online Contents | 2016

  14. Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets

    Cha, Kenny H / Hadjiiski, Lubomir / Samala, Ravi K et al. | Online Contents | 2016

  15. Development of the open‐source dose calculation and optimization toolkit matRad

    Wieser, Hans‐Peter / Cisternas, Eduardo / Wahl, Niklas et al. | Online Contents | 2017

  16. "Integration des neuen Lasers in das System und Entwicklung einer Software zur Bildverarbeitung" : Schlussbericht zum Vorhaben : im Rahmen des Eurostars Projekts E! 10689 FLIM-VERTICO - "Entwicklung eines neuartigen Bildgebungssystems basierend auf Fluoreszenzanregung durch ultrakurze Laserpulse zur in vivo Aufnahme mikroskopischer Querschnittsbilder durch die menschliche Haut" : Projektlaufzeit: 01.04.2017-30.09.2020

    Breunig, Hans Georg / König, Karsten / JenLab GmbH, Neuengönna | TIBKAT | 2020

  17. Deep learning of the sectional appearances of 3D CT images for anatomical structure segmentation based on an FCN voting method

    Zhou, Xiangrong / Takayama, Ryosuke / Wang, Song et al. | Online Contents | 2017

  18. AAPM TG 158: Measurement and calculation of doses outside the treated volume from external‐beam radiation therapy

    Kry, Stephen F / Bednarz, Bryan / Howell, Rebecca M et al. | Online Contents | 2017

  19. Using Deep Learning to Segment Breast and Fibroglanduar Tissue in MRI Volumes

    Dalmış, Mehmet Ufuk / Litjens, Geert / Holland, Katharina et al. | Online Contents | 2016

  20. Classification and evaluation strategies of auto‐segmentation approaches for PET: Report of AAPM task group No. 211

    Hatt, Mathieu / Lee, John A / Schmidtlein, Charles R et al. | Online Contents | 2017

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