Imaging Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hip-pocampal Segmentation

Evaluation of Uncertainty-Aware Multi-Software Ensembles for Hippocampal Segmentation

Accurate hippocampal segmentation can be a useful tool for diagnosing and monitoring neurological conditions such as Alzheimer’s disease and epilepsy. While numerous automated segmentation methods exist, their clinical adoption remains limited. Reliable uncertainty assessment can enhance trust and facilitate clinical t...

Artificial intelligence for white matter hyperintensity segmentation: performance, determinants, and clinical implications

AI demonstrates promising performance for automated WMH segmentation, with U-Net–based architectures showing superior accuracy, however, substantial heterogeneity highlights the need for standardized imaging protocols and robust external validation.

Biomedical Imaging Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification

Towards Trustworthy AI for Glioma Diagnosis: A Task-Aware Evaluation of Uncertainty Quantification

Uncertainty Quantification (UQ) is a key requirement for trustworthy AI in high-stakes medical image analysis. In this work, we evaluate UQ within a multi-task Deep Learning (DL) framework for MRI-based glioma diagnosis that performs tumor segmentation and predicts Isocitrate Dehydrogenase (IDH) mutation status, 1p/19q...

Bridging Accuracy and Interpretability: Explainable Deep Learning for Parkinson’s Disease Diagnosis from MRI

The results demonstrate that combining adaptive preprocessing, patient-wise evaluation, and explainable deep learning holds promise for MRI-based Parkinson’s disease detection under a preliminary, dataset-specific evaluation, though substantial performance variability remains across different subject selections, rather...

Benchmarking open-source automated thigh muscle MRI segmentation algorithms

Automatic segmentation of medical images could accelerate clinical and research workflows, yet existing tools are rarely compared independently on shared benchmarks, making it difficult to assess genuine progress. This is particularly consequential for limb muscle segmentation in magnetic resonance imaging (MRI), which...