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.

Small intestinal stromal tumor (SIST) is a common gastrointestinal tumor. Currently, SIST diagnosis relies on clinical radiologists reviewing CT images from medical imaging sensors. However, this method is inefficient and greatly affected by subjective factors. The automatic detection method for stromal tumors based on computer vision technology can better solve these problems. However, in CT images, SIST have different shapes and sizes, blurred edge texture, and little difference from surrounding normal tissues, which to a large extent challenges the use of computer vision technology for the automatic detection of stromal tumors. Furthermore, there are the following issues in the research on the detection and recognition of SIST. After analyzing mainstream target detection models on SIST data, it was discovered that there is an imbalance in the features at different levels during the feature fusion stage of the network model. Therefore, this paper proposes an algorithm, based on the attention balance feature pyramid (ABFP), for detecting SIST with unbalanced feature fusion in the target detection model. By combining weighted multi-level feature maps from the backbone network, the algorithm creates a balanced semantic feature map. Spatial attention and channel attention modules are then introduced to enhance this map. In the feature fusion stage, the algorithm scales the enhanced balanced semantic feature map to the size of each level feature map and enhances the original feature information with the original feature map, effectively addressing the imbalance between deep and shallow features. Consequently, the SIST detection model’s detection performance is significantly improved, and the method is highly versatile. Experimental results show that the ABFP method can enhance traditional target detection methods, and is compatible with various models and feature fusion strategies.