Although deep learning-based segmentation models have achieved impressive
performance on public benchmarks, generalizing well to unseen environments
remains a major challenge. To improve the model's generalization ability to the
new domain during evaluation, the test-time training (TTT) is a challenging
paradigm that adapts the source-pretrained model in an online fashion. Early
efforts on TTT mainly focus on the image classification task. Directly
extending these methods to semantic segmentation easily experiences unstable
adaption due to segmentation's inherent characteristics, such as extreme class
imbalance and complex decision spaces. To stabilize the adaptation process, we
introduce contrastive loss (CL), known for its capability to learn robust and
generalized representations. Nevertheless, the traditional CL operates in the
representation space and cannot directly enhance predictions. In this paper, we
resolve this limitation by adapting the CL to the output space, employing a
high temperature, and simplifying the formulation, resulting in a
straightforward yet effective loss function called Output Contrastive Loss
(OCL). Our comprehensive experiments validate the efficacy of our approach
across diverse evaluation scenarios. Notably, our method excels even when
applied to models initially pre-trained using domain adaptation methods on test
domain data, showcasing its resilience and adaptability.\footnote{Code and more
information could be found at~ https://github.com/dazhangyu123/OCL}

通过适应输出对比损失（OCL）方法，在图像语义分割中提高模型对新领域的泛化能力，验证了该方法在不同评估场景下的有效性。

利用输出对比损失进行语义分割的测试时间训练

Test-Time Training for Semantic Segmentation with Output Contrastive  Loss

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with
limited treatment options. This research proposes a workflow and deep
learning-based segmentation models to automatically assess tumor-vessel
involvement, a key factor in determining tumor resectability. Correct
assessment of resectability is vital to determine treatment options. The
proposed workflow involves processing CT scans to segment the tumor and
vascular structures, analyzing spatial relationships and the extent of vascular
involvement, which follows a similar way of working as expert radiologists in
PDAC assessment. Three segmentation architectures (nnU-Net, 3D U-Net, and
Probabilistic 3D U-Net) achieve a high accuracy in segmenting veins, arteries,
and the tumor. The segmentations enable automated detection of tumor
involvement with high accuracy (0.88 sensitivity and 0.86 specificity) and
automated computation of the degree of tumor-vessel contact. Additionally, due
to significant inter-observer variability in these important structures, we
present the uncertainty captured by each of the models to further increase
insights into the predicted involvement. This result provides clinicians with a
clear indication of tumor-vessel involvement and may be used to facilitate more
informed decision-making for surgical interventions. The proposed method offers
a valuable tool for improving patient outcomes, personalized treatment
strategies and survival rates in pancreatic cancer.

胰腺导管腺癌（PDAC）是一种具有有限治疗选择的高度侵袭性癌症。本研究提出一种工作流程和基于深度学习的分割模型，自动评估肿瘤 - 血管的参与程度，这是决定肿瘤可切除性的关键因素。肿瘤可切除性的正确评估对于确定治疗选择至关重要。所提出的工作流程包括处理 CT 扫描以分割肿瘤和血管结构，分析空间关系和血管参与的程度，该过程与 PDAC 评估中的专家放射科医生的工作方式相似。三种分割架构（nnU-Net，3D U-Net 和概率性 3D U-Net）在分割静脉、动脉和肿瘤方面具有高度准确性。分割使得可以自动检测肿瘤的参与程度，准确度高（0.88 敏感性和 0.86 特异性），并自动计算肿瘤 - 血管接触的程度。此外，由于这些重要结构的显著观察者差异性，我们提供了每个模型捕捉到的不确定性，进一步增加对预测参与度的洞察。这一结果为临床医生提供了肿瘤 - 血管参与的明确指示，并可用于促进外科干预的更理性决策。所提出的方法为改善胰腺癌患者的预后、个性化治疗策略和生存率提供了宝贵的工具。