  An estimated 15 million babies are born too early every year. Approximately 1
million children die each year due to complications of preterm birth (PTB).
Many survivors face a lifetime of disability, including learning disabilities
and visual and hearing problems. Although manual analysis of ultrasound images
(US) is still prevalent, it is prone to errors due to its subjective component
and complex variations in the shape and position of organs across patients. In
this work, we introduce a conceptually simple convolutional neural network
(CNN) trained for segmenting prenatal ultrasound images and classifying task
for the purpose of preterm birth detection. Our method efficiently segments
different types of cervixes in transvaginal ultrasound images while
simultaneously predicting a preterm birth based on extracted image features
without human oversight. We employed three popular network models: U-Net, Fully
Convolutional Network, and Deeplabv3 for the cervix segmentation task. Based on
the conducted results and model efficiency, we decided to extend U-Net by
adding a parallel branch for classification task. The proposed model is trained
and evaluated on a dataset consisting of 354 2D transvaginal ultrasound images
and achieved a segmentation accuracy with a mean Jaccard coefficient index of
0.923 $\pm$ 0.081 and a classification sensitivity of 0.677 $\pm$ 0.042 with a
3.49\% false positive rate. Our method obtained better results in the
prediction of preterm birth based on transvaginal ultrasound images compared to
state-of-the-art methods.
