Three-dimensional Magnetic Resonance Imaging Segmentation for Preoperative Planning in Gluteal Polymer Complications

Gluteal polymers may lead to chronic inflammation, recurrent infection, and progressive deformity, creating major challenges for diagnosis, surgical planning, and patient counseling. Although high-frequency dermatologic ultrasound is the gold standard for identifying and characterizing superficial aesthetic fillers, its diagnostic performance in the gluteal region is limited by insufficient penetration and reduced ability to differentiate deep subcutaneous and intramuscular compartments. Gluteal magnetic resonance imaging (MRI) overcomes these limitations by providing comprehensive multiplanar coverage, superior soft‑tissue contrast, and deep‑plane characterization. We present the case of a 42-year-old woman with recurrent inflammatory and infectious reactions following polymer injections performed in 2016. Despite multiple staged procedures and autologous fat grafting, chronic fibrosis and tethering persisted. Volumetric MRI allowed clear delineation of multilocular subcutaneous and superficial intramuscular polymer deposits, including inflammatory interfaces and postoperative scarring. Three‑dimensional (3D) segmentation was performed using 3D Slicer and enabled compartment‑specific segmentation with volumetric quantification of subcutaneous and intramuscular components. The resulting color‑coded 3D reconstruction provided high‑fidelity visualization of disease distribution and depth, aiding surgical strategy. Importantly, 3D segmentation enhanced patient counseling by illustrating, in a tangible and intuitive manner, the anatomical limitations that make complete polymer removal unfeasible-even with multiple surgical stages. This case highlighted the growing relevance of volumetric postcontrast MRI and 3D segmentation as practical and clinically impactful tools in the management of gluteal polymer complications. Their integration into routine imaging protocols may improve diagnostic accuracy, guide individualized surgical planning, align expectations, and support future developments in automated quantification and augmented‑reality surgical navigation.