NEC-NET : Segmentation and Feature Extraction Network for the Neurocranium in Early Childhood

Di Fan; Niharika Gajawelli; Athelia Paulli; Eryn Perry; Jeff Tanedo; Sean Deoni; Yalin Wang; Marius George Linguraru; Natasha Lepore

doi:10.1117/12.2670281

NEC-NET : Segmentation and Feature Extraction Network for the Neurocranium in Early Childhood

Proc SPIE Int Soc Opt Eng. 2022 Nov:12567:125671K. doi: 10.1117/12.2670281. Epub 2023 Mar 6.

Authors

Di Fan¹, Niharika Gajawelli¹, Athelia Paulli^{1

2}, Eryn Perry^{1

3}, Jeff Tanedo¹, Sean Deoni⁴, Yalin Wang⁵, Marius George Linguraru^{6

2}, Natasha Lepore^{1

7

8}

Affiliations

¹ CIBORG Lab, Department of Radiology, Children's Hospital Los Angeles, Los Angeles, CA, USA.
² Sheikh Zayed Institute for Pediatric Surgical Innovation. Children's National Hospital, Washington, D.C, USA.
³ Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA.
⁴ Bill and Melinda Gates Foundation, Seattle, WA, USA.
⁵ School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA.
⁶ Departments of Radiology and Pediatrics, George Washington University School of Medicine and Health Sciences, Washington, D.C, USA.
⁷ Department of Biomedical Engineering, University of Southern California, Los Angeles, CA, USA.
⁸ Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.

Abstract

In early life, the neurocranium undergoes rapid changes to accommodate the expanding brain. Neurocranial maturation can be disrupted by developmental abnormalities and environmental factors such as sleep position. To establish a baseline for the early detection of anomalies, it is important to understand how this structure typically grows in healthy children. Here, we designed a deep neural network pipeline NEC-NET, including segmentation and classification, to analyze the normative development of the neurocranium in T1 MR images from healthy children aged 12 to 60 months old. The pipeline optimizes the segmentation of the neurocranium and shows the preliminary results of age-based regional differences among infants.

Keywords: Neurocranium; brain; classification; segmentation.

Abstract

Grants and funding