The solidified state after the melting of the forming layer in the laser powder bed fusion (LPBF) directly reflects the final forming quality. Compared with the powder layer and the melt pool, it is easier to recognize and remove the defects of contour parts in time by monitoring and processing the solidified region after the melting of the forming layer. To explore the application of a solidified region image in defect contour detection of the forming layer, an improved image segmentation model based on TranUNet is designed to extract the image features of the solidified region as process data, on which basis this paper analyzes the similarities and differences between forming process data and CT scanning results data. Addressing the characteristics of large data volume and significant feature scale variation in the solidified region image obtained during the LPBF process, an SA-TransUNet semantic segmentation model integrating SE attention mechanism and ASPP multi-scale feature extraction module is developed to achieve high-precision solidified region image segmentation, with an $IoU$ and a dice coefficient index up to 94.24% and 97.02%, respectively. By extracting the solidified region image of the LPBF forming layer through this model and calculating the geometric feature values of its contour, a comparative analysis is conducted with the corresponding contour geometric feature values of the formed part CT scan image, which verifies the feasibility of the solidified region image extraction method proposed in this paper for contour defect detection.