Automation of ultrasonographic optic nerve sheath diameter measurement using convolutional neural networks

Abstract

Background and purpose

Ultrasonographic optic nerve sheath (ONS) diameter is a noninvasive intracranial pressure (ICP) surrogate. ICP is monitored invasively in specialized intensive care units. Noninvasive ICP monitoring is important in less specialized settings. However, noninvasive ICP monitoring using ONS diameter (ONSD) is limited by the need for experts to obtain and perform measurements. We aim to automate ONSD measurements using a deep convolutional neural network (CNN) with a novel masking technique.

Methods

We trained a CNN to reproduce masks that mark the ONS. The edges of the mask are defined by an expert. Eight models were trained with 1000 epochs per model. The Dice-similarity-coefficient-weighted averaged outputs of the eight models yielded the final predicted mask. Eight hundred and seventy-three images were obtained from 52 transorbital cine-ultrasonography sessions, performed on 46 patients with brain injuries. Eight hundred and fourteen images from 48 scanning sessions were used for training and validation and 59 images from four sessions for testing. Bland-Altman and Pearson linear correlation analyses were used to evaluate the agreement between CNN and expert measurements.

Results

Expert ONSD measurements and CNN-derived ONSD estimates had strong agreement (r = 0.7, p < .0001). The expert mean ONSD (standard deviation) is 5.27 mm (0.43) compared to CNN mean estimate of 5.46 mm (0.37). Mean difference (95% confidence interval, p value) is 0.19 mm (0.10-0.27 mm, p = .0011), and root mean square error is 0.27 mm.

Conclusion

A CNN can learn ONSD measurement using masking without image segmentation or landmark detection.