Machine learning for the automatic localisation of foetal body parts in cine-MRI scans

Bowles C, Nowlan NC, Hayat TTA, Malamateniou C, Rutherford M, Hajnal JV, Rueckert D, Kainz B (2015)

Publication Type: Conference contribution

Publication year: 2015

Journal

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Publisher: SPIE

Book Volume: 9413

Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Event location: Orlando, FL, USA

ISBN: 9781628415032

DOI: 10.1117/12.2081669

Abstract

Being able to automate the location of individual foetal body parts has the potential to dramatically reduce the work required to analyse time resolved foetal Magnetic Resonance Imaging (cine-MRI) scans, for example, for use in the automatic evaluation of the foetal development. Currently, manual preprocessing of every scan is required to locate body parts before analysis can be performed, leading to a significant time overhead. With the volume of scans becoming available set to increase as cine-MRI scans become more prevalent in clinical practice, this stage of manual preprocessing is a bottleneck, limiting the data available for further analysis. Any tools which can automate this process will therefore save many hours of research time and increase the rate of new discoveries in what is a key area in understanding early human development. Here we present a series of techniques which can be applied to foetal cine-MRI scans in order to first locate and then differentiate between individual body parts. A novel approach to maternal movement suppression and segmentation using Fourier transforms is put forward as a preprocessing step, allowing for easy extraction of short movements of individual foetal body parts via the clustering of optical flow vector fields. These body part movements are compared to a labelled database and probabilistically classified before being spatially and temporally combined to give a final estimate for the location of each body part.

Authors with CRIS profile

Bernhard Kainz

Involved external institutions

Imperial College London / The Imperial College of Science, Technology and Medicine GB United Kingdom (GB) King’s College London GB United Kingdom (GB)

How to cite

APA:

Bowles, C., Nowlan, N.C., Hayat, T.T.A., Malamateniou, C., Rutherford, M., Hajnal, J.V.,... Kainz, B. (2015). Machine learning for the automatic localisation of foetal body parts in cine-MRI scans. In Martin A. Styner, Sebastien Ourselin (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Orlando, FL, USA: SPIE.

MLA:

Bowles, Christopher, et al. "Machine learning for the automatic localisation of foetal body parts in cine-MRI scans." Proceedings of the Medical Imaging 2015: Image Processing, Orlando, FL, USA Ed. Martin A. Styner, Sebastien Ourselin, SPIE, 2015.

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