VP‐Nets : Efficient automatic localization of key brain structures in 3D fetal neurosonography

HighlightsWe propose VP‐Nets for brain structure localization in 3D fetal neurosonography.The proposed 2.5D CNN is memory efficient, producing a high resolution 3D output.Our model can handle structure overlap while keeping contextual information.Visualization of the trained VP‐Nets is described.VP‐Nets is compared with Random Forests and 3D U‐Nets. Graphical abstract Figure. No caption available. ABSTRACT Three‐dimensional (3D) fetal neurosonography is used clinically to detect cerebral abnormalities and to assess growth in the developing brain. However, manual identification of key brain structures in 3D ultrasound images requires expertise to perform and even then is tedious. Inspired by how sonographers view and interact with volumes during real‐time clinical scanning, we propose an efficient automatic method to simultaneously localize multiple brain structures in 3D fetal neurosonography. The proposed View‐based Projection Networks (VP‐Nets), uses three view‐based Convolutional Neural Networks (CNNs), to simplify 3D localizations by directly predicting 2D projections of the key structures onto three anatomical views. While designed for efficient use of data and GPU memory, the proposed VP‐Nets allows for full‐resolution 3D prediction. We investigated parameters that influence the performance of VP‐Nets, e.g. depth and number of feature channels. Moreover, we demonstrate that the model can pinpoint the structure in 3D space by visualizing the trained VP‐Nets, despite only 2D supervision being provided for a single stream during training. For comparison, we implemented two other baseline solutions based on Random Forest and 3D U‐Nets. In the reported experiments, VP‐Nets consistently outperformed other methods on localization. To test the importance of loss function, two identical models are trained with binary corss‐entropy and dice coefficient loss respectively. Our best VP‐Net model achieved prediction center deviation: 1.8 ± 1.4 mm, size difference: 1.9 ± 1.5 mm, and 3D Intersection Over Union (IOU): 63.2 ± 14.7% when compared to the ground truth. To make the whole pipeline intervention free, we also implement a skull‐stripping tool using 3D CNN, which achieves high segmentation accuracy. As a result, the proposed processing pipeline takes a raw ultrasound brain image as input, and output a skull‐stripped image with five detected key brain structures.