Recent advancements in biomedical image analysis have been significantly driven by the
Segment Anything Model (SAM). This transformative technology, originally developed for …

Medical image segmentation plays a crucial role in advancing healthcare systems for
disease diagnosis and treatment planning. The u-shaped architecture, popularly known as …

Brain metastases (BMs) are the most common intracranial tumor type and a significant
health concern, affecting approximately 10% to 30% of all oncological patients. Although …

The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT)
challenge aims to advance automated segmentation algorithms using the largest known …

Radiologists are tasked with interpreting a large number of images in a daily base, with the
responsibility of generating corresponding reports. This demanding workload elevates the …

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 …

Brain metastasis segmentation poses a significant challenge in medical imaging due to the
complex presentation and variability in size and location of metastases. In this study, we first …

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 …

Segmenting brain tumors is complex due to their diverse appearances and scales. Brain
metastases, the most common type of brain tumor, are a frequent complication of cancer …

Recently, deep learning (DL)-based methods have been proposed for the computational
reduction of gadolinium-based contrast agents (GBCAs) to mitigate adverse side effects …