从计算机断层扫描（CT）图像中准确分割各种临床病变是许多疾病诊断和治疗的关键任务。然而，当前的分割框架是针对特定疾病量身定制的，并且有限的框架可以检测和分割不同类型的病变。此外，分割视觉上不显眼的小规模肿瘤（如小肠间质瘤和胰腺肿瘤）是当前分割框架的另一个挑战性问题。我们提出的框架 CDI-NSTSEG 使用多尺度视觉信息和非局部目标挖掘有效地分割小的非显着肿瘤。 CDI-NSTSEG遵循临床医生的诊断流程，包括初步筛选、定位、细化和细分。具体来说，我们首先探索基于尺度空间理论提取三个不同尺度（1×、0.5×和1.5×）的独特特征。我们提出的尺度融合模块（SFM）分层融合特征以获得全面的表示，类似于临床诊断中的初步筛选。全局定位模块（GLM）采用非局部注意机制设计。它从融合特征中捕获通道和空间位置的远程语义依赖性。 GLM使我们能够从全局角度定位肿瘤并输出初始预测结果。最后，我们设计了层聚焦模块（LFM）来逐步细化初始结果。 LFM主要根据前景和背景特征进行上下文探索，逐层聚焦可疑区域，逐个元素进行加减，消除错误。我们的框架在小肠间质瘤和胰腺肿瘤数据集上实现了最先进的分割性能。 CDI-NSTSEG 在小肠间质瘤上的 Dice 性能优于最佳比较分割方法 7.38%。

To accurately segment various clinical lesions from computed tomography(CT) images is a critical task for the diagnosis and treatment of many diseases. However, current segmentation frameworks are tailored to specific diseases, and limited frameworks can detect and segment different types of lesions. Besides, it is another challenging problem for current segmentation frameworks to segment visually inconspicuous and small-scale tumors (such as small intestinal stromal tumors and pancreatic tumors). Our proposed framework, CDI-NSTSEG, efficiently segments small non-salient tumors using multi-scale visual information and non-local target mining. CDI-NSTSEG follows the diagnostic process of clinicians, including preliminary screening, localization, refinement, and segmentation. Specifically, we first explore to extract the unique features at three different scales (1×, 0.5×, and 1.5×) based on the scale space theory. Our proposed scale fusion module (SFM) hierarchically fuses features to obtain a comprehensive representation, similar to preliminary screening in clinical diagnosis. The global localization module (GLM) is designed with a non-local attention mechanism. It captures the long-range semantic dependencies of channels and spatial locations from the fused features. GLM enables us to locate the tumor from a global perspective and output the initial prediction results. Finally, we design the layer focusing module (LFM) to gradually refine the initial results. LFM mainly conducts context exploration based on foreground and background features, focuses on suspicious areas layer-by-layer, and performs element-by-element addition and subtraction to eliminate errors. Our framework achieves state-of-the-art segmentation performance on small intestinal stromal tumor and pancreatic tumor datasets. CDI-NSTSEG outperforms the best comparison segmentation method by 7.38% Dice on small intestinal stromal tumors.