Neural Architecture Search for Placenta Segmentation in 2D Ultrasound Images

Monitoring the placenta during pregnancy can lead to early diagnosis of anomalies by observing their characteristics, such as size, shape, and location. Ultrasound is a popular medical imaging technique used in placenta monitoring, whose advantages include the non-invasive feature, price, and accessibility. However, images from this domain are characterized by their noise. A segmentation system is required to recognize placenta features. U-Net architecture is a convolutional neural network that has become popular in the literature for medical image segmentation tasks. However, this type is a general-purpose network that requires great expertise to design and may only be applicable in some domains. The evolutionary computation overcomes this limitation, leading to the automatic design of convolutional neural networks. This work proposes a U-Net-based neural architecture search algorithm to construct convolutional neural networks applied in the placenta segmentation on 2D ultrasound images. The results show that the proposed algorithm allows a decrease in the number of parameters of U-Net, ranging from 80 to 98%. Moreover, the segmentation performance achieves a competitive level compared to U-Net, with a difference of 0.012 units in the Dice index.