Deep learning approach for cerebellum localization in prenatal ultrasound images

Ultrasound (US) has become one of the most common forms for medical imaging in clinical practice. It is a non-invasive and safe practice that allows obtaining images in real time. It is also a technology with important challenges such as low image quality and high variability (between manufacturers and institutions) [1]. This work aims to apply a fast and accurate deep learning architecture to detect and locate cerebellum in prenatal ultrasound images. Cerebellum biometry is used to estimate fetal age [2] and cerebellum segmentation could be applied to detect malformation [3]. YOLO (You Only Look Once) is a convolutional neural network (CNN) architecture for detection, classification and location of objects in images [4]. YOLO was innovative because it solved a regression problem to predict the location (coordinates and sizes) of bounding boxes and associated classes. We used 316 ultrasound scans of fetal brains and their respective cerebellar segmentations. From these, 78 images were randomly taken to be treated as test images and the rest were available to feed the trainings. Segmentation masks were converted to numerical descriptions of bounding boxes. To deal with small data set, transfer learning was done by initializing convolutional layers with weights pretrained on Imagenet [5]. We evaluated detection using F1 score and localization using average precision (AP) for 78 test images. Our best AP was 84.8% using 121 divisions or cells per image. Future work will focus on segmentation task assisted by localization.