Abstract
Pancreatic cancer, characterized by its notable prevalence and mortality rates, demands accurate lesion delineation for effective diagnosis and therapeutic interventions. The generalizability of extant methods is frequently compromised due to the pronounced variability in imaging and the heterogeneous characteristics of pancreatic lesions, which may mimic normal tissues and exhibit significant inter-patient variability. Thus, we propose a generalization framework that synergizes pixel-level classification and regression tasks, to accurately delineate lesions and improve model stability. This framework not only seeks to align segmentation contours with actual lesions but also uses regression to elucidate spatial relationships between diseased and normal tissues, thereby improving tumor localization and morphological characterization. Enhanced by the reciprocal transformation of task outputs, our approach integrates additional regression supervision within the segmentation context, bolstering the model's generalization ability from a dual-task perspective. Besides, dual self-supervised learning in feature spaces and output spaces augments the model's representational capability and stability across different imaging views. Experiments on 594 samples composed of three datasets with significant imaging differences demonstrate that our generalized pancreas segmentation results comparable to mainstream in-domain validation performance (Dice: 84.07%). More importantly, it successfully improves the results of the highly challenging cross-lesion generalized pancreatic cancer segmentation task by 9.51%. Thus, our model constitutes a resilient and efficient foundational technological support for pancreatic disease management and wider medical applications. The codes will be released at this https URL.
Abstract (translated)
胰腺癌因其高发病率和死亡率，需要准确地对病灶进行勾画以实现有效的诊断和治疗。现有的方法通常由于成像的显著变异性以及胰腺病变异质性特征（这些特征可能与正常组织相似，并表现出明显的患者间差异）而难以泛化应用。因此，我们提出了一种综合框架，该框架结合了像素级分类和回归任务，以准确勾画病灶并提高模型稳定性。此框架不仅力求使分割轮廓与实际病变对齐，还利用回归来阐明患病组织和正常组织之间的空间关系，从而改善肿瘤定位和形态特征描述。通过任务输出的相互转换，我们的方法在分割上下文中集成了额外的回归监督，从双任务的角度增强了模型的泛化能力。此外，在特征空间和输出空间中进行双重自监督学习增强了模型在不同成像视图下的表示能力和稳定性。 我们在一个由三组具有显著影像差异性的594个样本组成的实验数据集中进行了测试，结果表明我们的通用胰腺分割方法在主流领域内验证性能方面（Dice系数：84.07%）表现相当。更重要的是，在高挑战性的跨病灶泛化胰腺癌分割任务中提高了9.51%的表现。因此，我们的模型为胰腺疾病的管理和更广泛的医疗应用提供了稳健且高效的基础设施技术支撑。代码将在此网址发布。
URL
https://arxiv.org/abs/2505.01644
