Enhanced BoxInst for Weakly Supervised Liver Tumor Instance Segmentation in CT Images

Abstract

Accurate liver tumor detection and segmentation are essential for disease diagnosis and treatment planning. While traditional methods rely on pixel-level mask annotations in fully supervised training, weakly supervised techniques are gaining attention due to their reduced annotation requirements. In this study, we propose an enhanced version of BoxInst, called Enhanced BoxInst, which incorporates two key innovations: the position activation (PA) Module and the progressive mask generation (PMG) Module. The PA Module utilizes a Spatial Awareness (SA) Block to accurately locate tumor regions and encodes the location information to the segmentation branch through the Spatial Interaction Encoding (SIE) mechanism, thereby achieving cross-spatial feature interaction and ultimately improving the segmentation accuracy of liver tumors. Additionally, the PMG Module employs a feature decomposition scheme to refine tumor masks progressively from coarse to fine, accurately restoring the overall layout and boundary details of the tumor mask. Extensive experiments on the LiTS, AMU-Liver, and 3DIRCADb datasets demonstrate that Enhanced BoxInst outperforms existing methods in liver tumor instance segmentation. These results highlight the potential of our approach for practical use in medical image analysis, especially when only box annotations are available. The code is available at https://github.com/ssli23/Enhanced_BoxInst.