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| LEADER |
01316nam a2200193Ia 4500 |
| 001 |
10.3233-SHTI220785 |
| 008 |
220718s2022 CNT 000 0 und d |
| 020 |
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|a 18798365 (ISSN)
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| 245 |
1 |
0 |
|a 2D Anatomical Structure for COVID-19 Medical Images
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| 260 |
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0 |
|b NLM (Medline)
|c 2022
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| 856 |
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|z View Fulltext in Publisher
|u https://doi.org/10.3233/SHTI220785
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| 520 |
3 |
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|a For many clinical goals like surgical planning and radiotherapy treatment planning is necessary to understand the anatomical structures of the organ that is targeted. At the same time the 2D/3D shape of the organ is important to be reconstructed for the benefit of the doctors. For that reason, accurate segmentation techniques must be proposed to overcome the big data medical image storage problem. The main purpose of this work is to apply segmentation techniques for the definition of 3D organs (anatomical structures) when big data information has been stored and must be organized by the doctors for medical diagnosis. The processes would be implemented in the CT images from patients with COVID-19.
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| 650 |
0 |
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|a Anatomical structures
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| 650 |
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|a COVID-19
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| 650 |
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|a medical images
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| 650 |
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4 |
|a segmentation techniques
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| 700 |
1 |
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|a Diomidous, M.
|e author
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| 700 |
1 |
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|a Zimeras, S.
|e author
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| 773 |
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|t Studies in health technology and informatics
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