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arXiv Computer Vision By Linus Britt, Maximilian Nielsen, Susan Klapproth, Andre Kemmling, Michael H. Lev, Gabriel Broocks, Rene Werner, Thilo Sentker

Ischemic Stroke Segmentation and Net Water Uptake Quantification on Multicenter Non-Contrast CT Using Supervised Target-Domain Adaptation

Read the original on arXiv Computer Vision →

This study presents a domain-aware deep learning framework based on nnU-Net for segmenting ischemic stroke lesions on non‑contrast CT scans and quantifying net water uptake (NWU). Trained on data from Hamburg and the Acute Ischemic Stroke Dataset, the model was fine‑tuned on small target‑domain subsets from Boston and ISLES, achieving median Dice scores of 0.68 and 0.56 for lesions ≥30 mL, and an NWU mean absolute error of 1.37 percentage points on the Boston cohort. The results demonstrate that target‑domain adaptation can enable accurate NCCT‑only infarct segmentation and low‑error NWU estimation across heterogeneous multicenter datasets.

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.

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