Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images
IntroductionMonitoring the morphological features of the gestational sac (GS) and measuring the mean sac diameter (MSD) during early pregnancy are essential for predicting spontaneous miscarriage and estimating gestational age (GA). However, the manual process is labor-intensive and highly dependent...
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Pediatrics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fped.2024.1453302/full |
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| author | Hafiz Muhammad Danish Zobia Suhail Faiza Farooq |
| author_facet | Hafiz Muhammad Danish Zobia Suhail Faiza Farooq |
| author_sort | Hafiz Muhammad Danish |
| collection | DOAJ |
| description | IntroductionMonitoring the morphological features of the gestational sac (GS) and measuring the mean sac diameter (MSD) during early pregnancy are essential for predicting spontaneous miscarriage and estimating gestational age (GA). However, the manual process is labor-intensive and highly dependent on the sonographer's expertise. This study aims to develop an automated pipeline to assist sonographers in accurately segmenting the GS and estimating GA.MethodsA novel dataset of 500 ultrasound (US) scans, taken between 4 and 10 weeks of gestation, was prepared. Four widely used fully convolutional neural networks: UNet, UNet++, DeepLabV3, and ResUNet were modified by replacing their encoders with a pre-trained ResNet50. These models were trained and evaluated using 5-fold cross-validation to identify the optimal approach for GS segmentation. Subsequently, novel biometry was introduced to assess GA automatically, and the system's performance was compared with that of sonographers.ResultsThe ResUNet model demonstrated the best performance among the tested architectures, achieving mean Intersection over Union (IoU), Dice, Recall, and Precision values of 0.946, 0.978, 0.987, and 0.958, respectively. The discrepancy between the GA estimations provided by the sonographers and the biometry algorithm was measured at a Mean Absolute Error (MAE) of 0.07 weeks.ConclusionThe proposed pipeline offers a precise and reliable alternative to conventional manual measurements for GS segmentation and GA estimation. Furthermore, its potential extends to segmenting and measuring other fetal components in future studies. |
| format | Article |
| id | doaj-art-d4af12fa5e4d47b89986158c59e61411 |
| institution | OA Journals |
| issn | 2296-2360 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Pediatrics |
| spelling | doaj-art-d4af12fa5e4d47b89986158c59e614112025-08-20T02:37:06ZengFrontiers Media S.A.Frontiers in Pediatrics2296-23602024-12-011210.3389/fped.2024.14533021453302Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound imagesHafiz Muhammad Danish0Zobia Suhail1Faiza Farooq2Department of Computer Science, University of the Punjab, Lahore, PakistanDepartment of Computer Science, University of the Punjab, Lahore, PakistanDepartment of Radiology, University of Lahore Teaching Hospital, Lahore, PakistanIntroductionMonitoring the morphological features of the gestational sac (GS) and measuring the mean sac diameter (MSD) during early pregnancy are essential for predicting spontaneous miscarriage and estimating gestational age (GA). However, the manual process is labor-intensive and highly dependent on the sonographer's expertise. This study aims to develop an automated pipeline to assist sonographers in accurately segmenting the GS and estimating GA.MethodsA novel dataset of 500 ultrasound (US) scans, taken between 4 and 10 weeks of gestation, was prepared. Four widely used fully convolutional neural networks: UNet, UNet++, DeepLabV3, and ResUNet were modified by replacing their encoders with a pre-trained ResNet50. These models were trained and evaluated using 5-fold cross-validation to identify the optimal approach for GS segmentation. Subsequently, novel biometry was introduced to assess GA automatically, and the system's performance was compared with that of sonographers.ResultsThe ResUNet model demonstrated the best performance among the tested architectures, achieving mean Intersection over Union (IoU), Dice, Recall, and Precision values of 0.946, 0.978, 0.987, and 0.958, respectively. The discrepancy between the GA estimations provided by the sonographers and the biometry algorithm was measured at a Mean Absolute Error (MAE) of 0.07 weeks.ConclusionThe proposed pipeline offers a precise and reliable alternative to conventional manual measurements for GS segmentation and GA estimation. Furthermore, its potential extends to segmenting and measuring other fetal components in future studies.https://www.frontiersin.org/articles/10.3389/fped.2024.1453302/fullgestational sacautomatic segmentationfetal biometryearly pregnancyultrasound imagesdeep learning |
| spellingShingle | Hafiz Muhammad Danish Zobia Suhail Faiza Farooq Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images Frontiers in Pediatrics gestational sac automatic segmentation fetal biometry early pregnancy ultrasound images deep learning |
| title | Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images |
| title_full | Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images |
| title_fullStr | Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images |
| title_full_unstemmed | Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images |
| title_short | Deep learning-based automation for segmentation and biometric measurement of the gestational sac in ultrasound images |
| title_sort | deep learning based automation for segmentation and biometric measurement of the gestational sac in ultrasound images |
| topic | gestational sac automatic segmentation fetal biometry early pregnancy ultrasound images deep learning |
| url | https://www.frontiersin.org/articles/10.3389/fped.2024.1453302/full |
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