Automatic rat brain segmentation from MRI using statistical shape models and random forest

Simone Bendazzoli; Irene Brusini; Peter Damberg; Örjan Smedby; Leif Andersson; Chunliang Wang

doi:10.1117/12.2512409

15 March 2019 Automatic rat brain segmentation from MRI using statistical shape models and random forest

Simone Bendazzoli, Irene Brusini, Peter Damberg, Örjan Smedby, Leif Andersson, Chunliang Wang

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109492O (2019) https://doi.org/10.1117/12.2512409
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

In MRI neuroimaging, the shimming procedure is used before image acquisition to correct for inhomogeneity of the static magnetic field within the brain. To correctly adjust the field, the brain’s location and edges must first be identified from quickly-acquired low resolution data. This process is currently carried out manually by an operator, which can be time-consuming and not always accurate. In this work, we implement a quick and automatic technique for brain segmentation to be potentially used during the shimming. Our method is based on two main steps. First, a random forest classifier is used to get a preliminary segmentation from an input MRI image. Subsequently, a statistical shape model of the brain, which was previously generated from ground-truth segmentations, is fitted to the output of the classifier to obtain a model-based segmentation mask. In this way, a-priori knowledge on the brain’s shape is included in the segmentation pipeline. The proposed methodology was tested on low resolution images of rat brains and further validated on rabbit brain images of higher resolution. Our results suggest that the present method is promising for the desired purpose in terms of time efficiency, segmentation accuracy and repeatability. Moreover, the use of shape modeling was shown to be particularly useful when handling low-resolution data, which could lead to erroneous classifications when using only machine learning-based methods.

Citation Download Citation

Simone Bendazzoli, Irene Brusini, Peter Damberg, Örjan Smedby, Leif Andersson, and Chunliang Wang "Automatic rat brain segmentation from MRI using statistical shape models and random forest", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492O (15 March 2019); https://doi.org/10.1117/12.2512409

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