Precise segmentation of fetal head in ultrasound images using improved U-Net model

Autor: Vimala Nagabotu, Anupama Namburu
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: ETRI Journal, Vol 46, Iss 3, Pp 526-537 (2024)
Druh dokumentu: article
ISSN: 1225-6463
2233-7326
DOI: 10.4218/etrij.2023-0057
Popis: Monitoring fetal growth in utero is crucial to anomaly diagnosis. However, current computer-vision models struggle to accurately assess the key metrics (i.e., head circumference and occipitofrontal and biparietal diameters) from ultrasound images, largely owing to a lack of training data. Mitigation usually entails image augmentation (e.g., flipping, rotating, scaling, and translating). Nevertheless, the accuracy of our task remains insufficient. Hence, we offer a U-Net fetal head measurement tool that leverages a hybrid Dice and binary cross-entropy loss to compute the similarity between actual and predicted segmented regions. Ellipse-fitted two-dimensional ultrasound images acquired from the HC18 dataset are input, and their lower feature layers are reused for efficiency. During regression, a novel region of interest pooling layer extracts elliptical feature maps, and during segmentation, feature pyramids fuse fieldlayer data with a new scale attention method to reduce noise. Performance is measured by Dice similarity, mean pixel accuracy, and mean intersectionover- union, giving 97.90%, 99.18%, and 97.81% scores, respectively, which match or outperform the best U-Net models.
Databáze: Directory of Open Access Journals
Externí odkaz: