Pelvic fractures, often caused by high-impact trauma, frequently require surgical intervention. Imaging techniques such as CT and 2D X-ray imaging are used to transfer the surgical plan to the operating room through image registration, enabling quick intraoperative adjustments. Specifically, segmenting pelvic fractures from 2D X-ray imaging can assist in accurately positioning bone fragments and guiding the placement of screws or metal plates. In this study, we propose a novel deep learning-based category and fragment segmentation (CFS) framework for the automatic segmentation of pelvic bone fragments in 2D X-ray images. This framework consists of three consecutive steps. First, the category segmentation network extracts the left and right ilia and sacrum from X-ray images. Then, the fragment segmentation network further isolates the fragments in each masked bone region. Finally, the initially predicted bone fragments are reordered and refined through post-processing operations to form the final prediction. In the best-performing model, segmentation of pelvic fracture fragments achieves an intersection over union (IoU) of 0.91 for anatomical structures and 0.78 for fracture segmentation. Experimental results demonstrate that our CFS framework is effective in segmenting pelvic categories and fragments. For further research and development, the source code are publicly available at https://github.com/DaE-plz/CFSSegNet .