UNeXt’s capability for multi-target segmentation

Determine whether UNeXt can effectively handle multi-target segmentation scenarios in medical imaging beyond its demonstrated performance on single-lesion segmentation.

Background

In motivating the choice of YOLO-11 as the baseline, the paper contrasts real-time, instance-level segmentation needs in Transcranial Color-coded Doppler imaging with alternative architectures. UNeXt is highlighted as an efficient U-Net derivative optimized for parameters and FLOPs.

However, the authors explicitly note uncertainty about UNeXt’s suitability for multi-target segmentation, which is central to Circle of Willis analysis where multiple arterial structures can appear simultaneously. Establishing this capability would clarify whether UNeXt is a viable alternative to instance-oriented detectors like YOLO for such tasks.

References

However, while UNeXt demonstrates strong performance in single-lesion segmentation, its ability to handle multi-target scenarios remains uncertain.

— A Novel Attention-Augmented Wavelet YOLO System for Real-time Brain Vessel Segmentation on Transcranial Color-coded Doppler  (2508.13875 - Zhang et al., 19 Aug 2025) in Section II.B (Selection of YOLO-11 as the Baseline Framework)