Automatic Placenta Abnormality Detection Using Convolutional Neural Networks on Ultrasound Texture

Zoe Hu, Ricky Hu, Ryan Yan, Chantal Mayer, Robert N. Rohling, Rohit Singla

Abstract: Diseases related to the placenta, such as preeclampsia (PE) and fetal growth restriction (FGR), are major causes of mortality and morbidity. Diagnostic criteria of such diseases are defined by biomarkers, such as proteinuria, that appear in advanced gestational age. As placentally-mediated disease is often clinically unrecognized until later stages, accurate early diagnosis is required to allow earlier intervention, which is particularly challenging in low-resource areas without subspecialty clinicians. Proposed attempts at early diagnosis involve a combination of subjective and objective ultrasound placental assessments which have limited accuracy and high interobserver variability. Machine learning, particularly with convolutional neural networks, have shown potential in analyzing complex textural features in ultrasound imaging that may be predictive of disease. We propose a model utilizing a two-stage convolutional neural network pipeline to classify the presence of placental disease. The pipeline involves a segmentation stage to extract the placenta followed by a classification stage. We evaluated the pipeline on retrospectively collected placenta ultrasound scans and diagnostic outcomes of 321 patients taken by 18 sonographers and 3 ultrasound machines. Compared to existing clinical algorithms and neural networks, our classifier achieved significantly higher accuracy of 0.81 ± 0.02 (p < 0.05). Class activation maps were generated to identify potential abnormal regions of interest in placenta tissue. This study provides support that automated image analysis of ultrasound texture may assist physicians in early identification of placental disease, with potential benefits to low-resource environments.