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
This study demonstrates the technical feasibility of simultaneously segmenting 27 craniomaxillofacial anatomical structures from CBCT images using a single nnU-Net v2 model on a selected internal dataset and achieves strong segmentation performance.
I. Bayrakdar, Alican Kuran, Mehmet Uğurlu et al.· Dento maxillo facial radiolo...· 0 citations
A deep learning model, enhanced EfficientNetB7 U-Net (EEB7-UNet), is proposed to segment the fractured cervical vertebrae, which yields superior performance in terms of dice score index and Jaccard coefficient index compared to the other state of the art convolutional neural network used as an encoder in the U-Net.
A. Pandey, P. P, Kedarnath Senapati· IAES International Journal o...· 0 citations
The deep learning model demonstrated high accuracy in segmenting meningiomas and modest performance for peritumoral edema on preoperative magnetic resonance imaging (MRI) scans, support the potential utility of automated segmentation tools in clinical workflows.
D. de Wilde, Olivier Zanier, A. Alakmeh et al.· Neuroradiology· 0 citations
This work aims to train a multiclass vertebral subregion segmentation model with different model architectures, using a standard nnU-Net, a nnU-Net with residual encoder, and a shifted window U-Net transformer for semantic segmentation of thoracolumbar spine CTs.
Raffaele Da Mutten, Sven Theiler, Massimo Bottini et al.· Journal of imaging informati...· 0 citations
Although the deep learning model demonstrated accurate segmentation of vascular structures, further validation is needed to establish its utility for enabling accurate diagnosis of VAD in fast-paced clinical settings.
S. Zaveri, D. Zhang, E. Castellino et al.· medRxiv· 0 citations
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