Auto-segmentation of cerebral cavernous malformations using a convolutional neural network.

Journal: BMC medical imaging

Published Date: May 26, 2025

BACKGROUND: This paper presents a deep learning model for the automated segmentation of cerebral cavernous malformations (CCMs).

Chi-Jen Chou

Division of Neurosurgery, Department of Surgery, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan.
Huai-Che Yang

Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang-Ming University, Taipei, Taiwan.
Cheng-Chia Lee

Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang-Ming University, Taipei, Taiwan; Brain Research Center, National Yang-Ming University, Taipei, Taiwan.
Zhi-Huan Jiang

Department of Electrical Engineering, National Central University, Taoyuan, Taiwan.
Ching-Jen Chen

Department of Neurological Surgery, University of Virginia Health System, Charlottesville, Virginia, USA.
Hsiu-Mei Wu

Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan; School of Medicine, National Yang-Ming University, Taipei, Taiwan.
Chun-Fu Lin

Department of Neurosurgery, Neurological Institute, Taipei Veterans General Hospital, Taipei, Taiwan.
I-Chun Lai

School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Hsinchu, Taiwan.
Syu-Jyun Peng

Biomedical Electronics Translational Research Center, National Chiao Tung University, Hsin-Chu, Taiwan; Institute of Electronics, National Chiao Tung University, Hsin-Chu, Taiwan. Electronic address: blue.year@msa.hinet.net.

Adult Convolutional Neural Networks Deep Learning Female Hemangioma, Cavernous, Central Nervous System Humans Magnetic Resonance Imaging Male Middle Aged Neural Networks, Computer Radiosurgery

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