The local convex connected Patches algorithm (LCCP) suffers from the defects of super voxels crossing object boundaries and failing to utilize the regionally implicit concave-convex information. In order to improve the problems of low segmentation accuracy and object adhesion caused by the above defects, an improved algorithm combining connected domain segmentation is proposed. Firstly, the depth-adaptive superpixel segmentation (DASP) method is used to divide the image into superpixels based on depth information and normal vector angle; secondly, the concave-convexity of neighboring superpixels is determined based on the normal vector angle of superpixels, and all convex connected superpixels are combined to form the preliminary result; finally, the distance transformation and the watershed growth segmentation method based on superpixels are used to quickly segment the concave connected domain with large area into multiple convexregions. The segmentation is validated on the IC-BIN dataset, and the results show that the average segmentation accuracy (AP) is improved by 25% and 35% compared to LCCP and constrained plane cut (CPC), respectively, which significantly improves the under-segmentation problem.