Hybrid Technique for Estimating Fetal Head Circumference Using Ultrasound Imaging

Ameera Shafique; Zobia Suhail; Hafiz Muhammad Danish

Authors

Ameera Shafique Department of Computer Science, Faculty of Computing and Information Technology, University of the Punjab, Lahore, Pakistan.
Zobia Suhail Department of Computer Science, Faculty of Computing and Information Technology, University of the Punjab, Lahore, Pakistan.
Hafiz Muhammad Danish Department of Computer Science, University of Engineering and Technology, New Campus, Lahore, Pakistan.

Keywords:

Ultrasound, Imposed images, Head circumference, Residua U-net

Abstract

Introduction: Analysis of fetal head shape is crucial for assessing head growth and detecting abnormalities in fetuses. In traditional clinical practice, Head Circumference (HC) is determined by manually fitting an ellipse to the fetal skull based on 2D ultrasound images.

Novelty Statement: To address this, an automated method integrating image processing techniques with U-net variant have been developed to achieve maximum accuracy of fetal head circumference detection on HC18 dataset. This method aims to enhance precision in HC delineation, thereby improving clinical reliability.

Material and Method: This study proposed a method that combines image processing techniques (noise removal, edge detection, segmentation) with a Residual U-net model for detecting the boundary of the fetal skull using HC18 dataset.

Results and Discussion: The results of this method outperformed a simple residual u-net model in terms of accuracy. The proposed method is evaluating using the HC18 challenge dataset, achieving a Dice coefficient of 97.99%, a mean difference of 5.86 mm, and a mean Hausdorff distance of 0.56 mm compared to manual annotations. These results demonstrate the effectiveness of the proposed method in accurately delineating the fetal skull boundary.

Concluding remarks: Furthermore, the proposed method shows comparability with state-of-the-art techniques in the field.

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