What question did this study set out to answer?

The aim is to evaluate and benchmark segmentation methods directly on undersampled MRI data.

Understanding Benefits and Pitfalls of Current Methods for the Segmentation of Undersampled MRI Data

View Full Paper

Ask AI

Bookmark

Authors

This benchmark compares segmentation methods for undersampled MRI data, suggesting two-stage approaches outperform complex models.

The aim is to evaluate and benchmark segmentation methods directly on undersampled MRI data.
Conducted a comprehensive comparison of 7 segmentation approaches.
Focused on one-stage versus two-stage segmentation techniques.
Utilized two MRI datasets, including multi-coil k-space data and human-annotated ground truth.
Two-stage methods considering data-consistency achieved the highest segmentation scores.
Complex specialized methods did not outperform simpler, data-consistent approaches.

Morshuis et al. (2026) studied this question.

View Full Paper

Ask AI

Bookmark