Automatic meniscus segmentation using cascaded deep convolutional neural networks with 2D conditional random fields in knee MR images

Uju Jeon; Hyeonjin Kim; Helen Hong; Joon Ho Wang

doi:10.1117/12.2566450

1 June 2020 Automatic meniscus segmentation using cascaded deep convolutional neural networks with 2D conditional random fields in knee MR images

Uju Jeon, Hyeonjin Kim, Helen Hong, Joon Ho Wang

Author Affiliations +

Proceedings Volume 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020; 1151528 (2020) https://doi.org/10.1117/12.2566450
Event: International Workshop on Advanced Imaging Technologies 2020 (IWAIT 2020), 2020, Yogyakarta, Indonesia

Abstract

We propose an automatic segmentation method of meniscus using cascaded segmentation network consisting of 2D and 3D convolutional neural networks and 2D conditional random fields in knee MR images. First, 2D segmentation network and 2D conditional random fields are performed to narrow the field of view of the medial and lateral meniscus. Second, 3D segmentation network considering local and spatial information is performed to segment the medial and lateral meniscus. The 2D segmentation network showed under-segmentation inside the meniscus. The under-segmentation was prevented after 2D CRF, but over-segmentation occurred in nearby ligaments with similar intensity. The 3D segmentation network prevented under- and over-segmentation due to considering local and spatial information, and showed the best performance. The average dice similarity coefficients of proposed method were 92.27% and 90.27% at medial and lateral meniscus, showed better results of 4.78% and 9.96% at medial meniscus, 3.94% and 9.58% at lateral meniscus compared to the segmentation method using 2D U-Net results and combined 2D U-Net and 2D CRF, respectively. The medial meniscus shows higher accuracy than the lateral meniscus due to less leakage into the collateral ligament.

Citation Download Citation

Uju Jeon, Hyeonjin Kim, Helen Hong, and Joon Ho Wang "Automatic meniscus segmentation using cascaded deep convolutional neural networks with 2D conditional random fields in knee MR images", Proc. SPIE 11515, International Workshop on Advanced Imaging Technology (IWAIT) 2020, 1151528 (1 June 2020); https://doi.org/10.1117/12.2566450

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