Maysam Shahedi1, Catherine Y Spong2, James D Dormer1, Quyen N Do3, Yin Xi3,4, Matthew A Lewis3, Christina Herrera2, Ananth J Madhuranthakam3,5, Diane M Twickler3,2, Baowei Fei1,4,5. 1. University of Texas at Dallas, Department of Bioengineering, Dallas, Texas, United States. 2. University of Texas Southwestern Medical Center, Department of Obstetrics and Gynecology, Dallas, Texas, United States. 3. University of Texas Southwestern Medical Center, Department of Radiology, Dallas, Texas, United States. 4. University of Texas Southwestern Medical Center, Department of Clinical Science, Dallas, Texas, United States. 5. University of Texas Southwestern Medical Center, Advanced Imaging Research Center, Dallas, Texas, United States.
Abstract
Purpose: Magnetic resonance imaging has been recently used to examine the abnormalities of the placenta during pregnancy. Segmentation of the placenta and uterine cavity allows quantitative measures and further analyses of the organs. The objective of this study is to develop a segmentation method with minimal user interaction. Approach: We developed a fully convolutional neural network (CNN) for simultaneous segmentation of the uterine cavity and placenta in three dimensions (3D) while a minimal operator interaction was incorporated for training and testing of the network. The user interaction guided the network to localize the placenta more accurately. In the experiments, we trained two CNNs, one using 70 normal training cases and the other using 129 training cases including normal cases as well as cases with suspected placenta accreta spectrum (PAS). We evaluated the performance of the segmentation algorithms on two test sets: one with 20 normal cases and the other with 50 images from both normal women and women with suspected PAS. Results: For the normal test data, the average Dice similarity coefficient (DSC) was 92% and 82% for the uterine cavity and placenta, respectively. For the combination of normal and abnormal cases, the DSC was 88% and 83% for the uterine cavity and placenta, respectively. The 3D segmentation algorithm estimated the volume of the normal and abnormal uterine cavity and placenta with average volume estimation errors of 4% and 9%, respectively. Conclusions: The deep learning-based segmentation method provides a useful tool for volume estimation and analysis of the placenta and uterus cavity in human placental imaging.
Purpose: Magnetic resonance imaging has been recently used to examine the abnormalities of the placenta during pregnancy. Segmentation of the placenta and uterine cavity allows quantitative measures and further analyses of the organs. The objective of this study is to develop a segmentation method with minimal user interaction. Approach: We developed a fully convolutional neural network (CNN) for simultaneous segmentation of the uterine cavity and placenta in three dimensions (3D) while a minimal operator interaction was incorporated for training and testing of the network. The user interaction guided the network to localize the placenta more accurately. In the experiments, we trained two CNNs, one using 70 normal training cases and the other using 129 training cases including normal cases as well as cases with suspected placenta accreta spectrum (PAS). We evaluated the performance of the segmentation algorithms on two test sets: one with 20 normal cases and the other with 50 images from both normal women and women with suspected PAS. Results: For the normal test data, the average Dice similarity coefficient (DSC) was 92% and 82% for the uterine cavity and placenta, respectively. For the combination of normal and abnormal cases, the DSC was 88% and 83% for the uterine cavity and placenta, respectively. The 3D segmentation algorithm estimated the volume of the normal and abnormal uterine cavity and placenta with average volume estimation errors of 4% and 9%, respectively. Conclusions: The deep learning-based segmentation method provides a useful tool for volume estimation and analysis of the placenta and uterus cavity in human placental imaging.
Authors: Quyen N Do; Matthew A Lewis; Yin Xi; Ananth J Madhuranthakam; Sarah K Happe; Jodi S Dashe; Robert E Lenkinski; Ambereen Khan; Diane M Twickler Journal: J Magn Reson Imaging Date: 2019-08-09 Impact factor: 4.813
Authors: E Hafner; T Philipp; K Schuchter; B Dillinger-Paller; K Philipp; P Bauer Journal: Ultrasound Obstet Gynecol Date: 1998-08 Impact factor: 7.299
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