Development and validation of an automated MRI-based pipeline for temporal classification of intracerebral hemorrhage using U-Net segmentation and machine learning.
Aug 2026· Radiological Physics and Technology· 0 citations· 15 references
Medicine
TL;DR
The proposed automated MRI-based pipeline showed feasible performance for ICH temporal classification and may support objective MRI-based assessment of hemorrhage stage and may support objective MRI-based assessment of hemorrhage stage.
Meningioma is the most common benign primary intracranial tumor. Although its typical appearance on contrast-enhanced T1-weighted images is characteristic, tumor conspicuity and boundary definition vary substantially across MRI sequences, which complicates automated detection and delineation. Automatic meningioma detec...
A. Farré-Melero, Josep Puig, Daniel Alejandro Barrios-Reyes et al.· Journal of imaging informati...· 0 citations
The deep learning-based AI model enables automated segmentation and detection of FLLs on NC-MRI, with acceptable performance across different lesion sizes, including benign and malignant lesions.
Duo-Duo Zhang, Ke Wang, Peng-Sheng Wu et al.· Abdominal Radiology· 0 citations
INTRODUCTION
Meningiomas are the most common primary intracranial tumors and are frequently monitored over extended periods. Volumetric assessment typically requires manual segmentation, which is time-consuming and associated with interrater variability. This study aimed to develop and validate a deep learning-based mo...
D. de Wilde, Olivier Zanier, A. Alakmeh et al.· Neuroradiology· 0 citations
Accurate classification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) are essential for early diagnosis, treatment planning, and clinical decision-making. However, manual interpretation of MRI scans is time-consuming and prone to inter-observer variability. Deep learning techniques have emerged...
Rashmitha R. Nayak, Ramyashree, S. Raghavendra et al.· Discover Artificial Intellig...· 0 citations
An AI-based 3D brain tumor segmentation system based on a 3D U-Net architecture to segment brain tumors based on multi-classes with multi-modal MRI volumetric data to enhance interpretability and practical usability is introduced.
D. U. Latha, M. Padma, D. Rajeshwari et al.· Engineering, Technology &...· 0 citations
A unified framework that leverages graph neural networks and sequence-specific feature modeling for comprehensive ischemic stroke analysis from MRI is presented, designed to detect ischemic stroke, segment lesions, and estimate lesion volume from multi-sequence MRI data, while accommodating incomplete combinations of M...
Z. Lu, S. Uddin, S. Uribe et al.· medRxiv· 0 citations
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