Jul 2026· Journal of imaging informatics in medicine· 0 citations· 32 references
Medicine
TL;DR
ImprovedVertebroV5, a 3D U-Net architecture incorporating focal attention mechanisms, small-object detection modules, and pyramid pooling for context aggregation, demonstrated promising internal validation performance for automated VBAC segmentation on CBCT images and may support the future development of opportunistic screening tools in dental imaging.
The T1C-only model was non-inferior to the three-channel fusion model, offering a parsimonious single-sequence alternative that avoids registration-related confounds and yields volumetric and quantitative measurements in good to excellent agreement with manual segmentation.
V. Viertonen, Aapo Sirén, Julius Reima et al.· European Journal of Radiolog...· 0 citations
OBJECTIVE
To develop and evaluate an nnU-Net v2-based deep-learning model for fully automated multiclass segmentation of 27 craniomaxillofacial anatomical structures on cone-beam computed tomography (CBCT).
METHODS
This retrospective study included CBCT scans from 106 adult patients. Twenty-seven anatomical structure...
I. Bayrakdar, Alican Kuran, Mehmet Uğurlu et al.· Dento maxillo facial radiolo...· 0 citations
Accurate identification of vertebral fracture (VF) regions in computed tomography (CT) images is crucial for surgeons prior to treatment planning, but remains challenging due to irregular vertebral boundaries, low contrast, noise, and image unevenness. Recent advancements in deep learning have shown promising results c...
A. Pandey, P. P, Kedarnath Senapati· IAES International Journal o...· 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
Segmentations of the vertebral column that include anatomical subregions can be used for patient education, pedicle screw planning, or radiomic feature extraction for spinal surgery. Deep learning has proven successful in tackling medical image segmentation; therefore, we aim to train a multiclass vertebral subregion s...
Raffaele Da Mutten, Sven Theiler, Massimo Bottini et al.· Journal of imaging informati...· 0 citations
Purpose: The aims of this study were to develop an externally validated deep learning vascular segmentation model for digital enhancement of vertebral artery dissection detection on CTA and to assess clinical utility of the model through a paired crossover reader study. Materials and Methods: This retrospective, IRB-ap...
S. Zaveri, D. Zhang, E. Castellino et al.· medRxiv· 0 citations
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