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Biomedical Texture Analysis: Fundamentals, Applications, Tools and
Challenges describes the fundamentals and applications of
biomedical texture analysis (BTA) for precision medicine. It
defines what biomedical textures (BTs) are and why they require
specific image analysis design approaches when compared to more
classical computer vision applications. The fundamental properties
of BTs are given to highlight key aspects of texture operator
design, providing a foundation for biomedical engineers to build
the next generation of biomedical texture operators. Examples of
novel texture operators are described and their ability to
characterize BTs are demonstrated in a variety of applications in
radiology and digital histopathology. Recent open-source software
frameworks which enable the extraction, exploration and analysis of
2D and 3D texture-based imaging biomarkers are also presented. This
book provides a thorough background on texture analysis for
graduate students and biomedical engineers from both industry and
academia who have basic image processing knowledge. Medical doctors
and biologists with no background in image processing will also
find available methods and software tools for analyzing textures in
medical images.
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Head and Neck Tumor Segmentation and Outcome Prediction - Second Challenge, HECKTOR 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings (Paperback, 1st ed. 2022)
Vincent Andrearczyk, Valentin Oreiller, Mathieu Hatt, Adrien Depeursinge
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R2,371
Discovery Miles 23 710
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Ships in 10 - 15 working days
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This book constitutes the Second 3D Head and Neck Tumor
Segmentation in PET/CT Challenge, HECKTOR 2021, which was held in
conjunction with the 24th International Conference on Medical Image
Computing and Computer-Assisted Intervention, MICCAI 2021. The
challenge took place virtually on September 27, 2021, due to the
COVID-19 pandemic.The 29 contributions presented, as well as an
overview paper, 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 325
delineated PET/CT images was made available for training.
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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 (Paperback, 1st ed. 2021)
Vincent Andrearczyk, Valentin Oreiller, Adrien Depeursinge
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R1,580
Discovery Miles 15 800
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Ships in 10 - 15 working days
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This 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.
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