TL;DR
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.
Deep learning models performance in automated detection and segmentation by using different combinations of routine MRI sequences suggests that, when optimized, multi-sequence models could improve the performance of single-sequence models, which may facilitate future development of automated workflows for volumetric me...
Practical recommendations for automated segmentation and future validation requirements are outlined: favor self-configuring frameworks like nnU-Net when compute resources permit, consider lightweight 2D models such as YOLOv8 for fast screening, and ensure rigorous cross-site validation before potential clinical use.
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.
Purpose To investigate whether selective removal of vascular structures can improve lesion visibility and interpretability in maximum intensity projection (MIP) images derived from dynamic contrast-enhanced MRI. Materials and Methods A retrospective analysis was conducted using breast MRI scans from the Duke-Breast-Can...
To assess the influence of DWI quality and tumor complexity on the performance of a fully automated deep learning segmentation algorithm in a large and heterogeneous clinical dataset of rectal cancer MRIs. In this retrospective multicenter study, nnU-Nets were trained on baseline staging MRIs using either a dataset of...
The developed model achieves high sensitivity and precise automated gallstone segmentation on CT images and achieves overall sensitivities of 97.2%, 97.5%, 95.2%, 89.2%, and 98.2% across the training, validation, internal test, hold-out, and AMOS datasets.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
