A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.
Authors
Affiliations (3)
Affiliations (3)
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China.
- Department of Radiology, Fujian Maternal and Child Hospital, Fujian Key Laboratory of Women and Children's Critical Diseases Research, No.18 Daoshan Road, Gulou District, Fuzhou, 350001, Fujian, China.
- School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China. [email protected].
Abstract
To develop and evaluate a multi-stage computer-aided determination (CAD) method for automated antral follicle count (AFC) and dominant follicle identification using pelvic magnetic resonance imaging (MRI), aiming to overcome the limitations of MRI-based follicular analysis, including high annotation cost and dependence on manual interpretation. A total of 417 T2-weighted MRI slices from 124 patients were selected by radiologists, ensuring ovarian visibility and excluding poor quality images. The proposed CAD method incorporates a You Only Look Once version 11 detection model, which accurately selects MRI slices containing ovarian structures. This is followed by LCR-UNet, a novel segmentation model that integrates a lightweight atrous spatial pyramid pooling module and a cascaded decoder (CR-decoder) with channel shuffle convolutional block and residual block. The model achieved a Dice similarity coefficient of 0.8571 for follicle segmentation on MRI images. Based on pixel area ranking and ellipse fitting, dominant follicle identification reached an accuracy of 92.86% and an F1-score of 0.8929. Additionally, size-based filtering enabled automated counting of antral follicles with a precision of 82.91%. The proposed CAD method approaches the performance of experienced radiologists while reducing manual workload, offering a reliable tool for ovarian reserve and fertility assessment.