Exploring Multi-Pathology Brain Segmentation: From Volume-Based to Component-Based Deep Learning Analysis.

ABSTRACT: Detection and segmentation of brain abnormalities using Magnetic Resonance Imaging (MRI) is an important task that, nowadays, the role of AI algorithms as supporting tools is well established both at the research and clinical-production level. While the performance of the state-of-the-art models is increasing, reaching radiologists and other experts' accuracy levels in many cases, there is still a lot of research needed on the direction of in-depth and transparent evaluation of the correct results and failures, especially in relation to important aspects of the radiological practice: abnormality position, intensity level, and volume. In this work, we focus on the analysis of the segmentation results of a pre-trained U-net model trained and validated on brain MRI examinations containing fou

SUBMITTER: Stathopoulos I 

PROVIDER: S-EPMC11766070 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature