Accelerated muscle mass estimation from CT images through transfer learning

Background The cost of labeling to collect training data sets using deep learning is especially high in medical applications compared to other fields. Furthermore, due to variances in images depending on the computed tomography (CT) devices, a deep learning based segmentation model trained with a ce...

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Veröffentlicht in:	BMC medical imaging Jg. 24; H. 1; S. 271 - 18
Hauptverfasser:	Yoon, Seunghan, Kim, Tae Hyung, Jung, Young Kul, Kim, Younghoon
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 09.10.2024 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Abdomen Analysis Artificial intelligence Comparative analysis Computed tomography Convolutional neural network CT image segmentation CT imaging Datasets Deep Learning Diagnostic imaging Humans Image processing Image Processing, Computer-Assisted - methods Image segmentation Imaging Knowledge transfer Labeling Liver Machine learning Medical image segmentation Medical imaging Medical imaging equipment Medicine Medicine & Public Health Methods Muscle, Skeletal - diagnostic imaging Muscles Neural networks Performance degradation Performance evaluation Radiology Technology application Tomography, X-Ray Computed - methods Transfer learning South Korea Deep learning Convolutional neural network Medical image segmentation CT image segmentation
ISSN:	1471-2342, 1471-2342
Online-Zugang:	Volltext
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