Preoperative MRI-Based 3D Segmentation and Quantitative Modeling of Glandular and Adipose Tissues in Male...

Background: This study aimed to explore the application value of magnetic resonance imaging (MRI)-based three-dimensional segmentation and reconstruction technology for spatial structural identification and volumetric quantification of glandular and adipose tissues in bilateral gynecomastia (GM) patients undergoing surgical treatment, hoping to provide precise imaging data to support clinical surgical decision-making. Methods: A retrospective analysis was performed on preoperative MRI images and general clinical data of 52 patients with bilateral gynecomastia at the patient level (bilateral totals, N = 52) who underwent surgical treatment in the Department of Aesthetic and Reconstructive Breast Surgery, Plastic Surgery Hospital of Chinese Academy of Medical Sciences, from March 2023 to September 2024. All images were acquired using a SIEMENS Aera 1.5 T MRI scanner with T1-weighted three-dimensional fat-suppressed sequence (t1_fl3d_tra_spair). Semi-automatic segmentation and active contour modeling (Snake model) using ITK-SNAP 4.0 software were employed to independently identify glandular and adipose tissues, reconstruct accurate three-dimensional anatomical models, and quantitatively analyze tissue volumes. Results: The MRI-based three-dimensional segmentation and reconstruction method accurately distinguished glandular and adipose tissues in male breasts, establishing precise three-dimensional anatomical models with excellent reproducibility and operational consistency. Among the 52 patients with bilateral gynecomastia, glandular tissue volume exhibited a markedly non-normal distribution, with a median of 6.11 cm³ (IQR, 3.03-12.98 cm³). Adipose tissue volume followed a normal distribution with a mean of 1348.84 ± 494.97 cm³. The total breast tissue volume also showed a normal distribution, with a mean of 1361.97 ± 496.83 cm³. The proportion of glandular tissue in total breast volume was non-normally distributed with a median of 0.50% (IQR, 0.27-1.21%), while the proportion of adipose tissue was also non-normally distributed with a median of 99.50% (IQR, 98.79-99.73%). Conclusions: MRI combined with computer-assisted three-dimensional segmentation and reconstruction technology efficiently and accurately achieves spatial identification, three-dimensional modeling, and volumetric quantification of glandular and adipose tissues in patients with bilateral gynecomastia. It objectively reveals the spatial compositional characteristics of male breast tissues. This approach provides precise, quantitative data for clinical decision-making regarding surgical treatment of gynecomastia, featuring robust standardization and strong clinical applicability.