An improved U-Net for nerve fibre segmentation in confocal corneal microscopy images

Xinxin Zhou; Xinjian Chen; Shuanglang Feng; Fei Shi

doi:10.1117/12.2548257

10 March 2020 An improved U-Net for nerve fibre segmentation in confocal corneal microscopy images

Xinxin Zhou, Xinjian Chen, Shuanglang Feng, Fei Shi

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113131Z (2020) https://doi.org/10.1117/12.2548257
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Corneal confocal microscopy (CCM) is a new technique offering non-invasive and fast imaging useful for diagnosing and analyzing corneal diseases. The morphology of corneal nerve fibres can be clearly observed from CCM images. Segmentation and quantification of nerve fibres is important for analyzing corneal diseases such as diabetic peripheral neuropathy (DPN). In this paper, we propose an automated deep learning based method for corneal nerve fibre segmentation in CCM images. The main contributions of this paper are: (1)We add multi-scale split and concatenate (MSC) blocks to the decoding part of the four layer U-Net architecture. (2) A new loss function is applied that combining the Dice loss with the fibre length difference between the ground truth and the prediction. The method was tested on a dataset containing 90 CCM images from 4 normal eyes and 4 eyes with corneal diseases. The Dice coefficient of our approach can reach 87.96%, improves 1.6% compared with the baseline, and outperforms some existing deep networks for segmentation.

Citation Download Citation

Xinxin Zhou, Xinjian Chen, Shuanglang Feng, and Fei Shi "An improved U-Net for nerve fibre segmentation in confocal corneal microscopy images", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131Z (10 March 2020); https://doi.org/10.1117/12.2548257

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available