MR imaging is central to the assessment of tumor burden and changes over time in neuro- oncology. Several response assessment guidelines have been set forth by the Response …
Historically, clinician-derived contouring of tumors and healthy tissues has been crucial for radiation therapy (RT) planning. In recent years, advances in artificial intelligence (AI) …
Introduction Machine learning (ML) shows promise for the automation of routine tasks related to the treatment of pediatric low-grade gliomas (pLGG) such as tumor grading …
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children. The five-year survival rate for high-grade gliomas in children is less than …
Background: Fully-automatic skull-stripping and tumor segmentation are crucial for monitoring pediatric brain tumors (PBT). Current methods, however, often lack …
Significant advances have been made toward building accurate automatic segmentation models for adult gliomas. However, the performance of these models often degrades when …
Accurate segmentation of brain tumors in medical images is paramount for precise diagnosis and treatment planning. In this study, we introduce a robust approach for brain …