Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction
<p dir="ltr">Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structur...
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2022
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| _version_ | 1864513531109441536 |
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| author | Mahmood Alzubaidi (15740693) |
| author2 | Marco Agus (8032898) Uzair Shah (15740699) Michel Makhlouf (15740711) Khalid Alyafei (4578835) Mowafa Househ (9154124) |
| author2_role | author author author author author |
| author_facet | Mahmood Alzubaidi (15740693) Marco Agus (8032898) Uzair Shah (15740699) Michel Makhlouf (15740711) Khalid Alyafei (4578835) Mowafa Househ (9154124) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mahmood Alzubaidi (15740693) Marco Agus (8032898) Uzair Shah (15740699) Michel Makhlouf (15740711) Khalid Alyafei (4578835) Mowafa Househ (9154124) |
| dc.date.none.fl_str_mv | 2022-09-15T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.3390/diagnostics12092229 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Ensemble_Transfer_Learning_for_Fetal_Head_Analysis_From_Segmentation_to_Gestational_Age_and_Weight_Prediction/23004629 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Reproductive medicine image segmentation ensemble transfer learning fetal head gestational age estimated fetal weight ultrasound |
| dc.title.none.fl_str_mv | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW.</p><h2>Other Information</h2><p dir="ltr">Published in: Diagnostics<br>Licenses: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.3390/diagnostics12092229" target="_blank">http://dx.doi.org/10.3390/diagnostics12092229</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f6b64efe386c98adec03a311a17dd113 |
| identifier_str_mv | 10.3390/diagnostics12092229 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/23004629 |
| publishDate | 2022 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight PredictionMahmood Alzubaidi (15740693)Marco Agus (8032898)Uzair Shah (15740699)Michel Makhlouf (15740711)Khalid Alyafei (4578835)Mowafa Househ (9154124)Biomedical and clinical sciencesClinical sciencesReproductive medicineimage segmentationensemble transfer learningfetal headgestational ageestimated fetal weightultrasound<p dir="ltr">Ultrasound is one of the most commonly used imaging methodologies in obstetrics to monitor the growth of a fetus during the gestation period. Specifically, ultrasound images are routinely utilized to gather fetal information, including body measurements, anatomy structure, fetal movements, and pregnancy complications. Recent developments in artificial intelligence and computer vision provide new methods for the automated analysis of medical images in many domains, including ultrasound images. We present a full end-to-end framework for segmenting, measuring, and estimating fetal gestational age and weight based on two-dimensional ultrasound images of the fetal head. Our segmentation framework is based on the following components: (i) eight segmentation architectures (UNet, UNet Plus, Attention UNet, UNet 3+, TransUNet, FPN, LinkNet, and Deeplabv3) were fine-tuned using lightweight network EffientNetB0, and (ii) a weighted voting method for building an optimized ensemble transfer learning model (ETLM). On top of that, ETLM was used to segment the fetal head and to perform analytic and accurate measurements of circumference and seven other values of the fetal head, which we incorporated into a multiple regression model for predicting the week of gestational age and the estimated fetal weight (EFW). We finally validated the regression model by comparing our result with expert physician and longitudinal references. We evaluated the performance of our framework on the public domain dataset HC18: we obtained 98.53% mean intersection over union (mIoU) as the segmentation accuracy, overcoming the state-of-the-art methods; as measurement accuracy, we obtained a 1.87 mm mean absolute difference (MAD). Finally we obtained a 0.03% mean square error (MSE) in predicting the week of gestational age and 0.05% MSE in predicting EFW.</p><h2>Other Information</h2><p dir="ltr">Published in: Diagnostics<br>Licenses: <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.3390/diagnostics12092229" target="_blank">http://dx.doi.org/10.3390/diagnostics12092229</a></p>2022-09-15T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.3390/diagnostics12092229https://figshare.com/articles/journal_contribution/Ensemble_Transfer_Learning_for_Fetal_Head_Analysis_From_Segmentation_to_Gestational_Age_and_Weight_Prediction/23004629CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/230046292022-09-15T00:00:00Z |
| spellingShingle | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction Mahmood Alzubaidi (15740693) Biomedical and clinical sciences Clinical sciences Reproductive medicine image segmentation ensemble transfer learning fetal head gestational age estimated fetal weight ultrasound |
| status_str | publishedVersion |
| title | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| title_full | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| title_fullStr | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| title_full_unstemmed | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| title_short | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| title_sort | Ensemble Transfer Learning for Fetal Head Analysis: From Segmentation to Gestational Age and Weight Prediction |
| topic | Biomedical and clinical sciences Clinical sciences Reproductive medicine image segmentation ensemble transfer learning fetal head gestational age estimated fetal weight ultrasound |