Deep-learning-based segmentation of hydrocephalus brain ventricle from ultrasound

Yuli Wang; Yihao Liu; Shuwen Wei; Yuan Xue; Lianrui Zuo; Samuel W. Remedios; Zhangxing Bian; Michael Meggyesy; Jheesoo Ahn; Ryan P. Lee; Mark G. Luciano; Jerry L. Prince; Aaron Carass

doi:10.1117/12.3007668

2 April 2024 Deep-learning-based segmentation of hydrocephalus brain ventricle from ultrasound

Yuli Wang, Yihao Liu, Shuwen Wei, Yuan Xue, Lianrui Zuo, Samuel W. Remedios, Zhangxing Bian, Michael Meggyesy, Jheesoo Ahn, Ryan P. Lee, Mark G. Luciano, Jerry L. Prince, Aaron Carass

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 1292639 (2024) https://doi.org/10.1117/12.3007668
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Managing patients with hydrocephalus and cerebrospinal fluid disorders requires repeated head imaging. In adults, this is typically done with computed tomography (CT) or less commonly magnetic resonance imaging (MRI). However, CT poses cumulative radiation risks and MRI is costly. Transcranial ultrasound is a radiation-free, relatively inexpensive, and optionally point-of-care alternative. The initial use of this modality has involved measuring gross brain ventricle size by manual annotation. In this work, we explore the use of deep learning to automate the segmentation of brain right ventricle from transcranial ultrasound images. We found that the vanilla U-Net architecture encountered difficulties in accurately identifying the right ventricle, which can be attributed to challenges such as limited resolution, artifacts, and noise inherent in ultrasound images. We further explore the use of coordinate convolution to augment the U-Net model, which allows us to take advantage of the established acquisition protocol. This enhancement yielded a statistically significant improvement in performance, as measured by the Dice similarity coefficient. This study presents, for the first time, the potential capabilities of deep learning in automating hydrocephalus assessment from ultrasound imaging.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Yuli Wang, Yihao Liu, Shuwen Wei, Yuan Xue, Lianrui Zuo, Samuel W. Remedios, Zhangxing Bian, Michael Meggyesy, Jheesoo Ahn, Ryan P. Lee, Mark G. Luciano, Jerry L. Prince, and Aaron Carass "Deep-learning-based segmentation of hydrocephalus brain ventricle from ultrasound", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 1292639 (2 April 2024); https://doi.org/10.1117/12.3007668

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