Machine learning meets medical imaging: Learning and discovery of clinically useful information from images

TY - CHAP

T1 - Machine learning meets medical imaging

T2 - 4th Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013

AU - Rueckert, Daniel

AU - Wolz, Robin

AU - Aljabar, Paul

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Three-dimensional (3D) and four-dimensional (4D) imaging plays an increasingly important role in computer-assisted diagnosis, intervention and therapy. However, in many cases the interpretation of these images is heavily dependent on the subjective assessment of the imaging data by clinicians. Over the last decades image registration has transformed the clinical workflow in many areas of medical imaging. At the same time, advances in machine learning have transformed many of the classical problems in computer vision into machine learning problems. This paper will focus on the convergence of image registration and machine learning techniques for the discovery and quantification of clinically useful information from medical images. We will illustrate this with several examples such as the segmentation of neuro-anatomical structures, the discovery of biomarkers for neurodegenerative diseases and the quantification of temporal changes such as atrophy in Alzheimer’s disease.

AB - Three-dimensional (3D) and four-dimensional (4D) imaging plays an increasingly important role in computer-assisted diagnosis, intervention and therapy. However, in many cases the interpretation of these images is heavily dependent on the subjective assessment of the imaging data by clinicians. Over the last decades image registration has transformed the clinical workflow in many areas of medical imaging. At the same time, advances in machine learning have transformed many of the classical problems in computer vision into machine learning problems. This paper will focus on the convergence of image registration and machine learning techniques for the discovery and quantification of clinically useful information from medical images. We will illustrate this with several examples such as the segmentation of neuro-anatomical structures, the discovery of biomarkers for neurodegenerative diseases and the quantification of temporal changes such as atrophy in Alzheimer’s disease.

UR - http://www.scopus.com/inward/record.url?scp=84973092384&partnerID=8YFLogxK

M3 - Conference paper

AN - SCOPUS:84973092384

SN - 9781138000810

SP - 3

EP - 8

BT - Computational Vision and Medical Image Processing IV - Proceedings of Eccomas Thematic Conference on Computational Vision and Medical Image Processing, VIPIMAGE 2013

A2 - Manuel, João

A2 - Tavares, R.S.

A2 - Jorge, R.M. Natal

PB - CRC Press/Balkema

Y2 - 14 October 2013 through 16 October 2014

ER -