2021
DOI: 10.1007/978-3-030-87735-4_14
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Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture

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Cited by 3 publications
(3 citation statements)
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“…A fully automated DL model could perform this task rapidly and reliably, and eliminate interobserver variability [104][105][106] , potentially making placental biometry a useful imaging biomarker. Moreover, these algorithms are able to assess the location (anterior or posterior) 106 and appearance (normal or abnormal) 105 of the placenta. Segmentation DL models coupled with 3D ultrasound may provide additional information on the morphology and volume of the placenta 107,108 .…”
Section: Placentamentioning
confidence: 99%
See 1 more Smart Citation
“…A fully automated DL model could perform this task rapidly and reliably, and eliminate interobserver variability [104][105][106] , potentially making placental biometry a useful imaging biomarker. Moreover, these algorithms are able to assess the location (anterior or posterior) 106 and appearance (normal or abnormal) 105 of the placenta. Segmentation DL models coupled with 3D ultrasound may provide additional information on the morphology and volume of the placenta 107,108 .…”
Section: Placentamentioning
confidence: 99%
“…Placental biometry, which has been found to correlate with the occurrence of fetal smallness, pre-eclampsia and other adverse pregnancy outcomes 102,103 , is not performed routinely, because it is time-consuming and operator-dependent. A fully automated DL model could perform this task rapidly and reliably, and eliminate interobserver variability [104][105][106] , potentially making placental biometry a useful imaging biomarker. Moreover, these algorithms are able to assess the location (anterior or posterior) 106 and appearance (normal or abnormal) 105 of the placenta.…”
Section: Placentamentioning
confidence: 99%
“…However, sometimes placental-mediated diseases are not recognized until later stages. Hence, Hu et al [26] proposed a CNN pipeline to detect the presence of placental diseases. The model consists of segmentation of the placenta followed by classification utilizing a dataset containing US images of 321 patients (13,384 frames).…”
Section: Placentamentioning
confidence: 99%