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.

提出了一种新颖的无源主动域适应（SFADA）框架，用于优化鼻咽癌（NPC）中的肿瘤体积分割任务，通过选择特定目标域中的领域不变和领域特定样本进行标注和模型微调，既确保了数据隐私，又减轻了肿瘤学家的工作量。

多医院鼻咽癌肿瘤分割的双参照源无需活动领域适应

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal  Carcinoma Tumor Segmentation across Multiple Hospitals

Gross tumor volume (GTV) segmentation is a critical step in esophageal cancer
radiotherapy treatment planning. Inconsistencies across oncologists and
prohibitive labor costs motivate automated approaches for this task. However,
leading approaches are only applied to radiotherapy computed tomography (RTCT)
images taken prior to treatment. This limits the performance as RTCT suffers
from low contrast between the esophagus, tumor, and surrounding tissues. In
this paper, we aim to exploit both RTCT and positron emission tomography (PET)
imaging modalities to facilitate more accurate GTV segmentation. By utilizing
PET, we emulate medical professionals who frequently delineate GTV boundaries
through observation of the RTCT images obtained after prescribing radiotherapy
and PET/CT images acquired earlier for cancer staging. To take advantage of
both modalities, we present a two-stream chained segmentation approach that
effectively fuses the CT and PET modalities via early and late 3D
deep-network-based fusion. Furthermore, to effect the fusion and segmentation
we propose a simple yet effective progressive semantically nested network
(PSNN) model that outperforms more complicated models. Extensive 5-fold
cross-validation on 110 esophageal cancer patients, the largest analysis to
date, demonstrates that both the proposed two-stream chained segmentation
pipeline and the PSNN model can significantly improve the quantitative
performance over the previous state-of-the-art work by 11% in absolute Dice
score (DSC) (from 0.654 to 0.764) and, at the same time, reducing the Hausdorff
distance from 129 mm to 47 mm.

本文提出了一种利用 PET/CT 医学影像的深度学习处理方法，以提高食管癌治疗中的肿瘤分割精度，经 110 例食管癌患者验证，该方法较之前方法的 Dice 分数提高了 11％（从 0.654 到 0.764），并将 Hausdorff 距离从 129 毫米减少到 47 毫米。