文脈内自己学習による医用画像セグメンテーション

抄録

Annotation of medical images is crucial for assessing cancer treatment outcomes and defining radiotherapy targets. It also plays a key role in medical AI research as a preprocessing step for machine learning models. However, the heavy workload of medical professionals limits their capacity for extensive annotation tasks. To address this, we propose a method for segmenting sequential medical images with minimal annotation effort. Building on UniverSeg, which enables few-shot segmentation without additional training, our approach iteratively enhances segmentation by incorporating each inference result into the support set. Experiments on the HVSMR dataset show that our method outperforms baseline UniverSeg.