Fine segmentation of tiny blood vessel based on full connected conditional random field

Chenglong Wang; Masahiro Oda; Yasushi Yoshino; Tokunori Yamamoto; Kensaku Mori

doi:10.1117/12.2293486

2 March 2018 Fine segmentation of tiny blood vessel based on full connected conditional random field

Chenglong Wang, Masahiro Oda, Yasushi Yoshino, Tokunori Yamamoto, Kensaku Mori

Proceedings Volume 10574, Medical Imaging 2018: Image Processing; 105740K (2018) https://doi.org/10.1117/12.2293486
Event: SPIE Medical Imaging, 2018, Houston, Texas, United States

Abstract

In this paper, we present an efficient trainable conditional random field (CRF) model using a newly proposed scale-targeted loss function to improve the segmentation accuracy on tiny blood vessels in 3D medical images. Blood vessel segmentation is still a big challenge in medical image processing field due to its elongated structure and low contrast. Conventional local neighboring CRF model has poor segmentation performance on tiny elongated structures due to its poor capability capturing pairwise potentials. To overcome this drawback, we use a fully-connected CRF model to capture the pairwise potentials. This paper also introduces a new scale-targeted loss function aiming to improve the segmentation accuracy on tiny blood vessels. Experimental results on both phantom data and clinical CT data showed that the proposed approach contributes to the segmentation accuracy on tiny blood vessels. Compared to previous loss function, our proposed loss function improved about 10% sensitivity on phantom data and 14% on clinical CT data.

Conference Presentation

Citation Download Citation

Chenglong Wang, Masahiro Oda, Yasushi Yoshino, Tokunori Yamamoto, and Kensaku Mori "Fine segmentation of tiny blood vessel based on full connected conditional random field", Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740K (2 March 2018); https://doi.org/10.1117/12.2293486

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