Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal
  Carcinoma Tumor Segmentation across Multiple Hospitals
Hongqiu Wang Jian Chen Shichen Zhang Yuan He Jinfeng Xu Mengwan Wu Jinlan He Wenjun Liao Xiangde Luo
Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant
malignancy that predominantly impacts the head and neck area. Precise
delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring
effective radiotherapy for NPC. Despite recent methods that have achieved
promising results on GTV segmentation, they are still limited by lacking
carefully-annotated data and hard-to-access data from multiple hospitals in
clinical practice. Although some unsupervised domain adaptation (UDA) has been
proposed to alleviate this problem, unconditionally mapping the distribution
distorts the underlying structural information, leading to inferior
performance. To address this challenge, we devise a novel Sourece-Free Active
Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV
segmentation task. Specifically, we design a dual reference strategy to select
domain-invariant and domain-specific representative samples from a specific
target domain for annotation and model fine-tuning without relying on
source-domain data. Our approach not only ensures data privacy but also reduces
the workload for oncologists as it just requires annotating a few
representative samples from the target domain and does not need to access the
source data. We collect a large-scale clinical dataset comprising 1057 NPC
patients from five hospitals to validate our approach. Experimental results
show that our method outperforms the UDA methods and achieves comparable
results to the fully supervised upper bound, even with few annotations,
highlighting the significant medical utility of our approach. In addition,
there is no public dataset about multi-center NPC segmentation, we will release
code and dataset for future research.