Self-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation

Maria G. Baldeon Calisto; Susana K. Lai-Yuen

doi:10.1117/12.2543810

10 March 2020 Self-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation

Maria G. Baldeon Calisto, Susana K. Lai-Yuen

Proceedings Volume 11313, Medical Imaging 2020: Image Processing; 113131W (2020) https://doi.org/10.1117/12.2543810
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

Segmentation is a critical step in medical image analysis. Fully Convolutional Networks (FCNs) have emerged as powerful segmentation models achieving state-of-the art results in various medical image datasets. Network architectures are usually designed manually for a specific segmentation task so applying them to other medical datasets requires extensive expertise and time. Moreover, the segmentation requires handling large volumetric data that results in big and complex architectures. Recently, methods that automatically design neural networks for medical image segmentation have been presented; however, most approaches either do not fully consider volumetric information or do not optimize the size of the network. In this paper, we propose a novel self-adaptive 2D-3D ensemble of FCNs for 3D medical image segmentation that incorporates volumetric information and optimizes both the models performance and size. The model is composed of an ensemble of a 2D FCN that extracts intra-slice and long-range information, and a 3D FCN that exploits inter-slice information. The architectures of the 2D and 3D FCNs are automatically adapted to a medical image dataset using a multiobjective evolutionary based algorithm that minimizes both the expected segmentation error and number of parameters in the network. The proposed 2D-3D FCN ensemble was tested on the task of prostate segmentation on the image dataset from the PROMISE12 Grand Challenge. The resulting network is ranked in the top 10 submissions, surpassing the performance of other automatically-designed architectures while having 13.3x fewer parameters.

Citation Download Citation

Maria G. Baldeon Calisto and Susana K. Lai-Yuen "Self-adaptive 2D-3D ensemble of fully convolutional networks for medical image segmentation", Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 113131W (10 March 2020); https://doi.org/10.1117/12.2543810

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

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
11 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Image segmentation

Medical imaging

Prostate

Network architectures

Evolutionary algorithms

Convolution

Data modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years