Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection
Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great ves...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-11-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/23/8007 |
| _version_ | 1797507142599573504 |
|---|---|
| author | Siti Nurmaini Muhammad Naufal Rachmatullah Ade Iriani Sapitri Annisa Darmawahyuni Bambang Tutuko Firdaus Firdaus Radiyati Umi Partan Nuswil Bernolian |
| author_facet | Siti Nurmaini Muhammad Naufal Rachmatullah Ade Iriani Sapitri Annisa Darmawahyuni Bambang Tutuko Firdaus Firdaus Radiyati Umi Partan Nuswil Bernolian |
| author_sort | Siti Nurmaini |
| collection | DOAJ |
| description | Accurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans’ training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates. |
| first_indexed | 2024-03-10T04:45:24Z |
| format | Article |
| id | doaj.art-69f5dc1112474a5588a9fadf9203700e |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T04:45:24Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-69f5dc1112474a5588a9fadf9203700e2023-11-23T03:02:30ZengMDPI AGSensors1424-82202021-11-012123800710.3390/s21238007Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects DetectionSiti Nurmaini0Muhammad Naufal Rachmatullah1Ade Iriani Sapitri2Annisa Darmawahyuni3Bambang Tutuko4Firdaus Firdaus5Radiyati Umi Partan6Nuswil Bernolian7Intelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, IndonesiaIntelligent System Research Group, Faculty of Computer Science, Universitas Sriwijaya, Palembang 30139, IndonesiaFaculty of Medicine, Universitas Sriwijaya, Palembang 30139, IndonesiaDivision of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Mohammad Hoesin General Hospital, Palembang 30126, IndonesiaAccurate segmentation of fetal heart in echocardiography images is essential for detecting the structural abnormalities such as congenital heart defects (CHDs). Due to the wide variations attributed to different factors, such as maternal obesity, abdominal scars, amniotic fluid volume, and great vessel connections, this process is still a challenging problem. CHDs detection with expertise in general are substandard; the accuracy of measurements remains highly dependent on humans’ training, skills, and experience. To make such a process automatic, this study proposes deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach, which inherently segments the four standard heart views and detects the defect simultaneously. We conducted several experiments with 1149 fetal heart images for predicting 24 objects, including four shapes of fetal heart standard views, 17 objects of heart-chambers in each view, and three cases of congenital heart defect. The result showed that the proposed model performed satisfactory performance for standard views segmentation, with a 79.97% intersection over union and 89.70% Dice coefficient similarity. It also performed well in the CHDs detection, with mean average precision around 98.30% for intra-patient variation and 82.42% for inter-patient variation. We believe that automatic segmentation and detection techniques could make an important contribution toward improving congenital heart disease diagnosis rates.https://www.mdpi.com/1424-8220/21/23/8007fetal echocardiographydeep learningfetal heart standard viewheart defectinstance segmentation |
| spellingShingle | Siti Nurmaini Muhammad Naufal Rachmatullah Ade Iriani Sapitri Annisa Darmawahyuni Bambang Tutuko Firdaus Firdaus Radiyati Umi Partan Nuswil Bernolian Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection Sensors fetal echocardiography deep learning fetal heart standard view heart defect instance segmentation |
| title | Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection |
| title_full | Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection |
| title_fullStr | Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection |
| title_full_unstemmed | Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection |
| title_short | Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection |
| title_sort | deep learning based computer aided fetal echocardiography application to heart standard view segmentation for congenital heart defects detection |
| topic | fetal echocardiography deep learning fetal heart standard view heart defect instance segmentation |
| url | https://www.mdpi.com/1424-8220/21/23/8007 |
| work_keys_str_mv | AT sitinurmaini deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT muhammadnaufalrachmatullah deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT adeirianisapitri deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT annisadarmawahyuni deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT bambangtutuko deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT firdausfirdaus deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT radiyatiumipartan deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection AT nuswilbernolian deeplearningbasedcomputeraidedfetalechocardiographyapplicationtoheartstandardviewsegmentationforcongenitalheartdefectsdetection |