Automatic segmentation of the optic nerve in transorbital ultrasound images using a deep learning approach

被引：4

作者：

Meiburger, Kristen M. ^{[1
]}

Naldi, Andrea ^{[2
]}

Marzola, Francesco ^{[1
]}

Lochner, Piergiorgio ^{[3
]}

机构：

[1] Politecn Torino, Biolab, PolitoBIOMed Lab, Dept Elect & Telecommun, Turin, Italy

[2] Univ Torino, Dept Neurosci, Turin, Italy

[3] Saarland Univ Med Ctr, Homburg, Germany

来源：

INTERNATIONAL ULTRASONICS SYMPOSIUM (IEEE IUS 2021) | 2021年

关键词：

optic nerve diameter; optic nerve sheath diameter; segmentation; deep learning; CNN; STANDARDIZE;

D O I：

10.1109/IUS52206.2021.9593827

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

Transorbital sonography is able to provide reliable information about (a) intra-cranial pressure estimation through the optic nerve sheath diameter (ONSD) measurement, and (b) optic nerve atrophy in patients with multiple sclerosis through the optic nerve diameter (OND). In this study, we present the first method for the automatic measurement of the OND and ONSD using a deep learning technique (UNet with ResNet50 encoder) for the optic nerve segmentation. The dataset included 201 images from 50 patients. The automated measurements were compared with manual ones obtained by one operator. The mean error was equal to 0.07 +/- 0.34 mm and -0.07 +/- 0.67 mm, for the OND and ONSD, respectively. The developed system should aid in standardizing OND and ONSD measurements and reduce manual evaluation variability.

引用

页数：4

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