Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI

Yongkai Liu; Fatemeh Zabihollahy; Ran Yan; Brian Lee; Carla Janzen; Sherin U Devaskar; Kyunghyun Sung

doi:10.1002/jmri.28403

Evaluation of Spatial Attentive Deep Learning for Automatic Placental Segmentation on Longitudinal MRI

J Magn Reson Imaging. 2023 May;57(5):1533-1540. doi: 10.1002/jmri.28403. Epub 2022 Aug 16.

Authors

Yongkai Liu^{1

2}, Fatemeh Zabihollahy¹, Ran Yan^{1

3}, Brian Lee⁴, Carla Janzen⁵, Sherin U Devaskar⁴, Kyunghyun Sung^{1

2

3}

Affiliations

¹ Department of Radiological Sciences, University of California, Los Angeles, California, USA.
² Physics and Biology in Medicine IDP, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
³ Department of Bioengineering, Henry Samueli School of Engineering, University of California, Los Angeles, California, USA.
⁴ Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, California, USA.
⁵ Department of Obstetrics and Gynecology, David Geffen School of Medicine, University of California, Los Angeles, California, USA.

Abstract

Background: Automated segmentation of the placenta by MRI in early pregnancy may help predict normal and aberrant placenta function, which could improve the efficiency of placental assessment and the prediction of pregnancy outcomes. An automated segmentation method that works at one gestational age may not transfer effectively to other gestational ages.

Purpose: To evaluate a spatial attentive deep learning method (SADL) for automated placental segmentation on longitudinal placental MRI scans.

Study type: Prospective, single-center.

Subjects: A total of 154 pregnant women who underwent MRI scans at both 14-18 weeks of gestation and at 19-24 weeks of gestation, divided into training (N = 108), validation (N = 15), and independent testing datasets (N = 31).

Field strength/sequence: A 3 T, T2-weighted half Fourier single-shot turbo spin-echo (T2-HASTE) sequence.

Assessment: The reference standard of placental segmentation was manual delineation on T2-HASTE by a third-year neonatology clinical fellow (B.L.) under the supervision of an experienced maternal-fetal medicine specialist (C.J. with 20 years of experience) and an MRI scientist (K.S. with 19 years of experience).

Statistical tests: The three-dimensional Dice similarity coefficient (DSC) was used to measure the automated segmentation performance compared to the manual placental segmentation. A paired t-test was used to compare the DSCs between SADL and U-Net methods. A Bland-Altman plot was used to analyze the agreement between manual and automated placental volume measurements. A P value < 0.05 was considered statistically significant.

Results: In the testing dataset, SADL achieved average DSCs of 0.83 ± 0.06 and 0.84 ± 0.05 in the first and second MRI, which were significantly higher than those achieved by U-Net (0.77 ± 0.08 and 0.76 ± 0.10, respectively). A total of 6 out of 62 MRI scans (9.6%) had volume measurement differences between the SADL-based automated and manual volume measurements that were out of 95% limits of agreement.

Data conclusions: SADL can automatically detect and segment the placenta with high performance in MRI at two different gestational ages.

Level of evidence: 4 TECHNICAL EFFICACY STAGE: 2.

Keywords: automated placental segmentation; convolutional neural network; spatial attentive deep learning.

Publication types

Research Support, N.I.H., Extramural

MeSH terms

Deep Learning*
Female
Humans
Image Processing, Computer-Assisted / methods
Magnetic Resonance Imaging / methods
Placenta
Pregnancy
Prospective Studies

Grants and funding

U01 HD087221/HD/NICHD NIH HHS/United States