Abstract

For visual tasks like ultrasound (US) scanning, experts direct their gaze towards regions of task-relevant information. Therefore, learning to predict the gaze of sonographers on US videos captures the spatio-temporal patterns that are important for US scanning. The spatial distribution of gaze points on video frames can be represented through heat maps termed saliency maps. Here, we propose a temporally bidirectional model for video saliency prediction (BDS-Net), drawing inspiration from modern theories of human cognition. The model consists of a convolutional neural network (CNN) encoder followed by a bidirectional gated- recurrent-unit recurrent convolutional network (GRU-RCN) decoder. The temporal bidirectionality mimics human cognition, which simultaneously reacts to past and predicts future sensory inputs. We train the BDS-Net alongside spatial and temporally one-directional comparative models on the task of predicting saliency in videos of US abdominal circumference plane detection. The BDS-Net outperforms the comparative models on four out of five saliency metrics. We present a qualitative analysis on representative examples to explain the model’s superior performance.

@InProceedings{
  author={Droste, Richard and Cai, Yifan and Sharma, Harshita and Chatelain, Pierre and Papageorghiou, Aris T. and Noble, J. Alison},
  title={Towards Capturing Sonographic Experience: Cognition-Inspired Ultrasound Video Saliency Prediction},
  booktitle={Medical Image Understanding and Analysis (MIUA)},
  series={CCIS},
  year={2019},
  publisher={Springer}
}