Automatic segmentation of MRI in prospective breast volume evaluation: Comparison of different assessments for
Kuok Kun Chan1, Chin-Jung Feng2, Zen-Chung Shih3
1Division of Plastic and Reconstructive Surgery, Department of Surgery, Taipei Veterans General Hospital, Taiwan.
Background:
Assessment of breast volume is essential in preoperative planning of immediate breast reconstruction (IBR) surgery to achieve satisfactory cosmetic outcome. This study introduced a breast volume measurement tool that can be used to perform automatic segmentation of magnetic resonance images (MRI) and calculation of breast volume. We compared the accuracy and reliability of this measurement method with four other conventional modalities.
Methods:
Patients who were scheduled to undergo mastectomy with IBR between 2016 and 2021 were enrolled in the study. Five different breast volume assessments, including automatic segmentation of MRI, manual segmentation of MRI, 3D surface imaging, mammography, and the BREAST-V formula, were used to evaluate different breast volumes. The results were validated using water displacement volumes of the mastectomy specimens.
Results:
In this pilot study, a total of 50 female patients met the inclusion criteria and contributed 54 breast specimens to the volumetric analysis. There was a strong linear association between the MRI and water displacement methods (automatic segmentation: r = 0.911, p < 0.001; manual segmentation: r = 0.924, p < 0.001), followed by 3D surface imaging (r = 0.858, p < 0.001), mammography (r = 0.841, p < 0.001), and Breast-V formula (r = 0.838, p < 0.001). Breast volumes measured using automatic and manual segmentation of MRI had lower mean relative errors (30.3% ± 22.0% and 28.9% ± 19.8, respectively) than 3D surface imaging (38.9% ± 31.2), Breast-V formula (44.8% ± 25.8), and mammography (60.3% ± 37.6).
Conclusion:
Breast volume assessment using the MRI methods had better accuracy and reliability than the other methods used in our study. Breast volume measurement using automatic segmentation of MRI could be more efficient compared to the conventional methods.
