Background Manual detection of brain metastases is both laborious and inconsistent, driving
the need for more efficient solutions. Accordingly, our systematic review and meta-analysis …

The translation of AI-generated brain metastases (BM) segmentation into clinical practice
relies heavily on diverse, high-quality annotated medical imaging datasets. The BraTS …

Objective To thoroughly analyze factors affecting the generalization ability of deep learning
algorithms on brain tumor detection and segmentation models. Patients and Methods We …

Background The purpose of this study was to compare the segmentation performances of
the 2D, 2.5 D, and 3D networks for maxillary sinuses (MSs) and lesions inside the maxillary …

Manual segmentation of lesions, required for radiotherapy planning and follow-up, is time-
consuming and error-prone. Automatic detection and segmentation can assist radiologists in …

Abstract 3D UNet has achieved high brain tumor segmentation performance but requires
high computation, large memory, abundant training data, and has limited interpretability. As …

Brain metastases represent the most common ma-lignancy affecting the adult central
nervous sys-tem (Le Rhun et al., 2021), affecting an estimated 20–40% of patients with …

Recent progress in deep learning (DL) is producing a new generation of tools across
numerous clinical applications. Within the analysis of brain tumors in magnetic resonance …

In recent years, the surge in glioma brain tumor cases has positioned it as the 10th most
prevalent tumor affecting individuals across diverse age groups. Gliomas, characterized by …

Background Deep learning‐based segmentation of brain metastases relies on large
amounts of fully annotated data by domain experts. Semi‐supervised learning offers …