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Abstract #3887

Automatic Segmentation and Normal Dataset of Fetal Body from Magnetic Resonance Imaging

Bella Fadida-Specktor1, Dafna Ben Bashat2,3, Daphna Link Sourani2, Netanell Avisdris1,2, Elka Miller4, Liat Ben Sira3,5, and Leo Joskowicz1
1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Haifa, Israel, 2Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel, 3Sackler Faculty of Medicine & Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel, 4Medical Imaging, Children’s Hospital of Eastern Ontario, University of Ottawa, Ottawa, ON, Canada, 5Division of Pediatric Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel

Weight estimation is of great importance in assessing fetal development, yet unavailable in routine fetal MRI. The aim of this study was to develop an automatic fetal body segmentation method and to create a large dataset of volumetric body measurements of normal fetuses. Automatic fetal body segmentation was performed on data obtained from two clinical sites, three MRI systems and two sequences. Using a neural network trained for each sequence, high performance was achieved for both of them. A database of normal fetal volumes with a wide range of gestational age was created and was consistent with ultrasound growth chart.

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