Population-Driven Synthesis of Personalized Cranial Development From Cross-Sectional Pediatric CT Images

Objective: Predicting normative pediatric growth is crucial to identify developmental anomalies. While traditional statistical and computational methods have shown promising results predicting personalized development, they either rely on statistical assumptions that limit generalizability or requir...

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Vydáno v:	IEEE transactions on biomedical engineering Ročník 72; číslo 9; s. 2732 - 2741
Hlavní autoři:	Liu, Jiawei, Xing, Fuyong, Elkhill, Connor, Linguraru, Marius George, Miles, Randy C., Cruz-Guerrero, Ines A., Porras, Antonio R.
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	United States IEEE 01.09.2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Age Biomedical imaging Child Child, Preschool Children Computed tomography Cranial Cross-Sectional Studies Customization Datasets Decoding Deep Learning Encoders-Decoders Female Generative adversarial network Generative adversarial networks Humans identity preservation Infant Infant, Newborn Male Patients Pediatrics personalized growth prediction Phenotypes Predictive models Representations Skull - diagnostic imaging Skull - growth & development Statistical distributions Statistics Synthesis Three-dimensional displays Tomography, X-Ray Computed - methods
ISSN:	0018-9294, 1558-2531, 1558-2531
On-line přístup:	Získat plný text
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