Segmentation of uterus and myoma from MRI image based on deep learning

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Dice coefficients: Uterus 81.55%, Endometrium 73.49%, Myoma 67.34%

• Primary Outcome: Automated segmentation accuracy using Dice coefficient

• Nnt Or Sensitivity: Sensitivity and specificity not reported; only Dice overlap coefficients provided

• Confidence Interval: Not reported

Clinical Application

Requires specialized MRI protocols and computational infrastructure; may be challenging in smaller centers Could complement existing radiology services; relevant for PBS-funded MRI studies for myoma assessment; aligns with RANZCOG guidelines for myoma evaluation Women with suspected or confirmed uterine myomas requiring MRI assessment

Abstract

OBJECTIVE: Myoma is a common gynecologic condition with abnormal muscular and fibrotic tissue growth in the uterus. Compared with sonography, magnetic resonance imaging (MRI) provides more detailed information with a better diagnostic accuracy. However, the interpretation of the imagery is shrewdness- and experience-dependent. The therapeutic strategy of myoma can be directly affected by the relative location and size in the uterus. Hence, this study aimed to generate an objective standardized modality in interpreting the imagery. MATERIALS AND METHODS: Deep learning (DL) was used to train two models using MRI imagery labeled by radiologists: 1) Model Uterus + Endometrium: segmentation of uterus and endometrium; 2) Model Myoma: segmentation of myoma. The myomas were classified based on their relative position in the uterus according to FIGO criteria. Each set of MRI image sequence was obtained from uterine sagittal sections with 5.5-mm interval of 30 slices per case. The dataset was divided into training, validation, and test sets. The number of images was first increased by data augmentation before implementing a DL semantic segmentation DeepLabV3+model. Cross entropy and Dice function were used for the loss and evaluation functions, respectively. RESULTS: The Dice values of uterus, endometrium and myoma segmentation reached 81.55%, 73.49% and 67.34%, respectively. Thus, the 3-D prediction of the uterine, endometrial and myomatous regions had a positive effect on determining the classification of myomas. CONCLUSION: DL can be used as a tool to improve the diagnosis and classification of uterine myoma. This model can also be applied to other gynecologic diseases.

References

1. 1.Lin, D. Q., Yeh, L. R., Chang, Y., Chen, J. H., Yu, Y. C., Chang, C. C., Chiang, J. Y., & Huang, S. J. (2024). Segmentation of uterus and myoma from MRI image based on deep learning. Taiwanese Journal of Obstetrics & Gynecology, 63(3), 123-129. https://doi.org/10.1016/j.tjog.2024.05.030