Pelvic fractures pose a clinical challenge in women due to the complexity of the pelvic structure and adjacent organs. This paper introduces a step-by-step deep learning pipeline for segmenting pelvic fractures in CT imaging, specifically for female health. Using anatomical and fracture surface segmentation, our method addresses fracture pattern variability. We highlight the clinical impact on women’s physical, sexual, and reproductive health and describe our technical methods. Our results show the potential for computer-aided imaging to enhance diagnostic accuracy and segmentation for women’s pelvic trauma, especially in urgent clinical situations. Our code, models, and 54 newly annotated cases are available at https://github.com/Jarartur/multi-step-pelvic-fractures .