Head and neck tumor segmentation = f...
3D Head and Neck Tumor Segmentation in PET/CT Challenge (2020 :)

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  • Head and neck tumor segmentation = first Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
  • 紀錄類型: 書目-電子資源 : Monograph/item
    正題名/作者: Head and neck tumor segmentation/ edited by Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge.
    其他題名: first Challenge, HECKTOR 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020 : proceedings /
    其他題名: HECKTOR 2020
    其他作者: Andrearczyk, Vincent.
    團體作者: 3D Head and Neck Tumor Segmentation in PET/CT Challenge
    出版者: Cham :Springer International Publishing : : 2021.,
    面頁冊數: x, 109 p. :ill. (some col.), digital ;24 cm.
    內容註: Overview of the HECKTOR Challenge at MICCAI 2020: Automatic Head and Neck Tumor Segmentation in PET/CT -- Two-stage approach for segmenting gross tumor volume in head and neck cancer with CT and PET imaging -- The Head and Neck Tumor Segmentation Using nnU-Net with Spatial and Channel 'Squeeze & Excitation' Blocks -- Squeeze-and-Excitation Normalization for Automated Delineation of Head and Neck Primary Tumors in Combined PET and CT Images -- Automatic Head and Neck Tumor Segmentation in PET/CT with Scale Attention Network -- Iteratively Refine the Segmentation of Head and Neck Tumor in FDG-PET and CT images -- Combining CNN and Hybrid Active Contours for Head and Neck Tumor Segmentation in CT and PET images -- Oropharyngeal Tumour Segmentation using Ensemble 3D PET-CT Fusion Networks for the HECKTOR Challenge -- Patch-based 3D UNet for Head and Neck Tumor Segmentation with an Ensemble of Conventional and Dilated Convolutions -- Tumor Segmentation in Patients with Head and Neck Cancers using Deep Learning based-on Multi-modality PET/CT Images -- GAN-based Bi-modal Segmentation using Mumford-Shah Loss: Application to Head and Neck Tumors in PET-CT Images.
    Contained By: Springer Nature eBook
    標題: Diagnostic imaging - Congresses. - Data processing -
    電子資源: https://doi.org/10.1007/978-3-030-67194-5
    ISBN: 9783030671945
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