Wachinger et al. 2014 - BrainPrint: Identifying Subject by Their Brain (paper summary)

This paper was presented at the MICCAI conference in 2014
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paper summary of: 
Wachinger, C., Golland, P., & Reuter, M. (2014, September). Brainprint: Identifying subjects by their brain. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 41-48). Springer, Cham.

Abstract

Introducing BrainPrint, a compact and discriminative representation of anatomical structures in the brain. BrainPrint captures shape information of an ensemble of cortical and subcortical structures by solving the 2D and 3D Laplace-Beltrami operator on triangular (boundary) and tetrahedral (volumetric) meshes. We derive a robust classifier for this representation that identifies the subject in a new scan, based on a database of brain scans. In an example dataset containing over 3000 MRI scans, we show that BrainPrint captures unique information about the subject’s anatomy and permits to correctly classify a scan with an accuracy of over 99.8%. All processing steps for obtaining the compact representation are fully automated making this processing framework particularly attractive for handling large datasets.

Introduction

This paper is focused on developing a subject-specific brain signature that is stable across time and insensitive to image artifacts. It must also be holistic, so the subject can be identified even if one part changes. 

To fulfill these requirements, the authors introduce BrainPrint, a holistic representation of brain anatomy, consisting of shape info on cortical and subcortical structures. They use the Laplace-Beltrami operator to calculate an eigenvalue spectrum that represents the shape of the object. 

The structures they use come from Freesurfer and consist of both the boundary surfaces (pial and white matter) and the individual structures. To get a robust identification, they let each brain structure vote independently for the individual. The classifier they use can identify previously encountered subject with high accuracy and can also determine whether the subject exists in the database already or not. 

BrainPrint is particularly beneficial for large datasets. First, they segment the anatomical structures from the anatomical T1 image, the they essentially transfer this info into a compact and discriminative representation. The representation takes up very little memory and allows for easier calculations and comparisons that the original whole image. 

Related Work

3D objects can be represented as a 3D volume or a 2D boundary (surface) 

BrainPrint is based off of shape-DNA by Reuter and use both the volume (tetrahedral mesh) and surface meshes. 

Although shape DNA has been used with single structures before, this is the first paper to apply it to multiple structures and cortical structures.  

Shape Descriptor

The shape descriptor used is the Laplace Beltrami spectrum. See paper for details on its computation.​

Classifier

They built a classifier that combines results from weaker classifiers working on specific brain structures. They use one that uses all structures and one that combines votes from the structures and uses the mode vote. 

Results

They use the ADNI dataset and calculate a shape descriptor for 36 subcortical structures and 8 cortical structures. They also calculate the asymmetry between the left/right cortical structures. 
They achieved an accuracy of 99.8% using 50 eigenvalues on all features with the asymmetry features.