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

Research output: Chapter in Book/Report/Conference proceedingConference paper

Christopher Bowles, Niamh C. Nowlan, Tayyib T A Hayat, Christina Malamateniou, Mary Rutherford, Joseph V. Hajnal, Daniel Rueckert, Bernhard Kainz

Original languageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging - Proceedings of SPIE
PublisherSPIE
Volume9413
ISBN (Print)9781628415032
DOIs
Publication statusPublished - 2015
EventMedical Imaging 2015: Image Processing - Orlando, United States
Duration: 24 Feb 201526 Feb 2015

Conference

ConferenceMedical Imaging 2015: Image Processing
CountryUnited States
CityOrlando
Period24/02/201526/02/2015

King's Authors

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.

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