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_a10.1007/978-3-030-67194-5 _2doi |
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_aHead and Neck Tumor Segmentation _h[electronic resource] : _bFirst Challenge, HECKTOR 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Proceedings / _cedited by Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge. |
250 | _a1st ed. 2021. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2021. |
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300 |
_aX, 109 p. 32 illus., 29 illus. in color. _bonline resource. |
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_atext _btxt _2rdacontent |
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_acomputer _bc _2rdamedia |
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_aonline resource _bcr _2rdacarrier |
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490 | 1 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12603 |
|
505 | 0 | _aOverview 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. | |
520 | _aThis book constitutes the First 3D Head and Neck Tumor Segmentation in PET/CT Challenge, HECKTOR 2020, which was held in conjunction with the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020, in Lima, Peru, in October 2020. The challenge took place virtually due to the COVID-19 pandemic. The 2 full and 8 short papers presented together with an overview paper in this volume were carefully reviewed and selected form numerous submissions. This challenge aims to evaluate and compare the current state-of-the-art methods for automatic head and neck tumor segmentation. In the context of this challenge, a dataset of 204 delineated PET/CT images was made available for training as well as 53 PET/CT images for testing. Various deep learning methods were developed by the participants with excellent results. | ||
650 | 0 |
_aImage processing _xDigital techniques. |
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650 | 0 | _aComputer vision. | |
650 | 0 | _aBioinformatics. | |
650 | 0 | _aMachine learning. | |
650 | 0 | _aSoftware engineering. | |
650 | 1 | 4 | _aComputer Imaging, Vision, Pattern Recognition and Graphics. |
650 | 2 | 4 | _aComputational and Systems Biology. |
650 | 2 | 4 | _aMachine Learning. |
650 | 2 | 4 | _aSoftware Engineering. |
700 | 1 |
_aAndrearczyk, Vincent. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aOreiller, Valentin. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aDepeursinge, Adrien. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030671938 |
776 | 0 | 8 |
_iPrinted edition: _z9783030671952 |
830 | 0 |
_aImage Processing, Computer Vision, Pattern Recognition, and Graphics, _x3004-9954 ; _v12603 |
|
856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-67194-5 |
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