Automatic segmentation and volumetric analysis of intracranial hemorrhages in brain CT images.

Journal: European journal of radiology

PMID: 39978270

Abstract

BACKGROUND: Intracranial hemorrhages (ICH) are life-threatening conditions that require rapid detection and precise subtype classification. Automated segmentation and volumetric analysis using deep learning can enhance clinical decision-making.

Authors

Murat Yüce

Icahn School of Medicine at Mount Sinai Biomedical Engineering and Imaging Institute, New York, USA.
Samet Öztürk

Esenler Obstetrics & Gynecology and Pediatrics Hospital, Radiology Clinic, Istanbul, Turkiye. Electronic address: drozturksamet@gmail.com.
Gül Gizem Pamuk

Bagcilar Training and Research Hospital, Radiology Clinic, Istanbul, Turkiye.
Candan Varlık

Bagcilar Training and Research Hospital, Radiology Clinic, Istanbul, Turkiye.
Ahmet Tan Cimilli

Bagcilar Training and Research Hospital, Radiology Clinic, Istanbul, Turkiye.

Keywords

Aged Deep Learning Female Humans Imaging, Three-Dimensional Intracranial Hemorrhages Male Middle Aged Radiographic Image Interpretation, Computer-Assisted Sensitivity and Specificity Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39978270)

Automatic segmentation and volumetric analysis of intracranial hemorrhages in brain CT images.

Abstract

Authors

Keywords

External Resources

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