Automatic Follicle Counting From Ultrasound Images of Ovaries Using MARDSE-UNET Model

Detecting ovarian structures in ultrasound images is essential in gynecological and reproductive medicine. An automated detection system can serve as a valuable tool for physicians and assist in complex ultrasound interpretations. This study presents a CNN-based object detector designed to segment and count follicle regions in ovarian ultrasound images. Automated identification of ovarian follicles can aid in diagnosing conditions such as infertility, Polycystic Ovarian Syndrome (PCOS), ovarian cancer, and other reproductive health issues. The proposed model, Multi-Attention Residual Dilated UNet with Squeeze and Excitation (MARDSE-UNet), integrates residual UNet, dilated UNet, and squeeze-and-excitation blocks to enhance follicle detection performance. MARDSE-UNet achieved exceptional results, with 98.69% accuracy, 97.89% precision, 97.7% recall, an F1-score of 86.97%, and Intersection over Union (IoU) of 95.66% in follicle detection using 5-fold cross-validation. The USOVA3D dataset was used for experimentation. The model also incorporates a novel preprocessing stage to address noise and low contrast issues, as well as a post-processing stage to refine edges and extract features such as area, perimeter, and diameter of follicles for a more comprehensive performance comparison. The proposed model outperformed traditional CNN models and other state-of-the-art methods in comparative evaluations.