Model Effect or Label Effect? Refined Annotations and a Human-Referenced Benchmark for Pulmonary Embolism Segmentation

The study investigates how refined annotations versus model training changes affect pulmonary embolism segmentation performance. By re‑annotating 149 CT pulmonary angiography cases and evaluating two pretrained nnU-Net models, the authors find that improving annotation quality increases the Dice similarity coefficient (DSC) by 0.143–0.188, far exceeding the 0.028 DSC change from altering training datasets. A new human‑referenced benchmark model (nnPE) was trained and publicly released, though it performed below all annotators in paired comparisons.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

• computer-vision
• benchmarks