We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us
GLOBETECH PUBLISHING LLC

Download Mobile App




Deep Learning Algorithm Performs Automatic Segmentation of Neonatal Brains from MR Images

By MedImaging International staff writers
Posted on 29 Feb 2024
Print article
Image: Researchers have created a new MRI brain extraction tool for neonates (Photo courtesy of 123RF)
Image: Researchers have created a new MRI brain extraction tool for neonates (Photo courtesy of 123RF)

Magnetic Resonance Imaging (MRI) is a vital tool in medical diagnostics, particularly because of its high-resolution images and superior soft tissue contrast, which make it crucial for brain evaluations. This imaging technique is particularly vital for neonates, especially for assessing neonatal encephalopathy, where it helps in understanding the presence and pattern of brain injuries for better prognostication and treatment planning. The integration of artificial intelligence (AI) and machine learning (ML) has significantly enhanced the predictive accuracy of functional outcomes in infants using MRI data. A crucial step in preparing data for ML analysis of brain MRI is brain extraction or skull-stripping. However, the development of extraction algorithms for neonatal brains has been limited. To address this gap, researchers have now introduced an automated deep learning-based algorithm for neonatal brain MRI extraction.

A collaborative effort by researchers from the University of California, San Francisco (UCSF) and Duke University Medical Center (Durham, NC, USA) has led to the creation of ANUBEX. This deep learning algorithm is specifically designed for automatic segmentation of neonatal brains from MRI scans. The development of ANUBEX, an automated neonatal nnU-Net brain MRI extractor, utilized various MRI sequences such as T1-weighted, T2-weighted, and diffusion-weighted imaging (DWI) from neonatal MRI studies.

The researchers found that ANUBEX maintains consistent performance when trained on sequence-agnostic or motion-degraded MRI scans, though it showed slightly decreased effectiveness on preterm brains. ANUBEX’s deep learning-based approach has demonstrated accurate performance across both high- and low-resolution MRIs, offering rapid computational processing. This accuracy in brain tissue segmentation is crucial for subsequent image analysis and volumetric measurements. Future directions for this research include expanding the evaluation of ANUBEX’s accuracy beyond the neonatal age range to include young children and adults. Additionally, there is a need to assess the model's effectiveness on brains with diverse structural pathologies.

Related Links:
UCSF
Duke University Medical Center 

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Color Doppler Ultrasound System
KC20
C-Arm with FPD
Digiscan V20 / V30
New
Pre-Op Planning Solution
Sectra 3D Trauma

Print article
Radcal

Channels

Nuclear Medicine

view channel
Image: The new SPECT/CT technique demonstrated impressive biomarker identification (Journal of Nuclear Medicine: doi.org/10.2967/jnumed.123.267189)

New SPECT/CT Technique Could Change Imaging Practices and Increase Patient Access

The development of lead-212 (212Pb)-PSMA–based targeted alpha therapy (TAT) is garnering significant interest in treating patients with metastatic castration-resistant prostate cancer. The imaging of 212Pb,... Read more

General/Advanced Imaging

view channel
Image: The Tyche machine-learning model could help capture crucial information. (Photo courtesy of 123RF)

New AI Method Captures Uncertainty in Medical Images

In the field of biomedicine, segmentation is the process of annotating pixels from an important structure in medical images, such as organs or cells. Artificial Intelligence (AI) models are utilized to... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2024 Globetech Media. All rights reserved.