Video Experimental Relacionado

Updated: May 6, 2026

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes

Published on: March 3, 2018

Un método de determinación asistida por computadora basado en aprendizaje profundo para la identificación de

Jingde Hong¹, Chunxia Chen², Ming Li¹

¹School of Optoelectronic and Communication Engineering, Xiamen University of Technology, No.600 Ligong Road, Jimei District, Xiamen, 361024, Fujian, China.

Biomedical engineering online

|February 20, 2026

Abstracto

Object:

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.

Materials And Methods:

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.

Results:

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%.

Discussion:

The proposed CAD method approaches the performance of experienced radiologists while reducing manual workload, offering a reliable tool for ovarian reserve and fertility assessment.

Palabras clave:

Recuento de folículos antrales Determinación asistida por computadora Aprendizaje profundo Folículo dominante Imágenes por resonancia magnética

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