Good view frames from ultrasonography (USG) video containing ONS diameter using state-of-the-art deep learning architectures.

This paper presents an automated method for detection of the diagnostically prominent frames containing optic nerve sheath (ONS) from ocular ultrasonography video using deep learning; such frames are referred to as "Good View" frames in this paper. Vivid acquisition and measurement of diagnostic features during ultrasound imaging is a challenging task; it needs a highly skilled and experienced medical expert. Automated detection of the Good View frame and the subsequent automatic measurement of optic nerve sheath diameter (ONSD), predicting elevated intracranial pressure (ICP) status, will eliminate the need for frequent intervention of a medical expert for continuous monitoring and ICP status in traumatic patients. In the presented work, the proposed model automatically detects the appropriate frames containing ONS, from an ultrasound video, by using faster region-based CNN (Faster R-CNN) object detection model. The region proposal detection network finds the ONS by using bounding boxes. In addition, three CNN-based architectures are used for its feature extraction. Finally, SoftMax classifier classifies the ONS containing Good View frame. The Inceptionv2, ResNet50, and ResNet101 architectures are then compared by utilizing the optimized learning rate and epoch parameters for the CNN model so as to provide better detection of the Good View frame. The performance of the developed module has been analyzed by proposing a grading criterion of the Good View frame. Based on the detection score and mean opinion score, an USG frame is considered a Good View for a 95-99% detection score, and this Good View frame is used for measuring the ONSD value. It is found that Faster R-CNN ResNet101 (model 3) is an optimal model in terms of sensitivity and specificity for Good View frame detection at a learning rate of 0.0003. The sensitivity and specificity of this model are obtained as 90.41 and 91.45, respectively. Furthermore, the ONSD value is measured from Good View-detected frames using an automated algorithm involving image processing and computational methods. Considering the Good View frame (detection score 95-99), the algorithm-generated ONSD values are compared with the radiologist's measured value of ONSD to validate the findings; a small percent root mean square difference (PRD) of 0.501 is found between these values, which is strong indicative of the accuracy of algorithm generated ONSD measurement using automatically detected Good View ocular USG frames.