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Automatic rat brain segmentation from MRI using statistical shape models and random forest
KTH Royal Inst Technol, Dept Biomed Engn & Hlth Syst, Halsovagen 11, S-14157 Huddinge, Sweden.
KTH Royal Inst Technol, Dept Biomed Engn & Hlth Syst, Halsovagen 11, S-14157 Huddinge, Sweden;Karolinska Inst, Dept Neurobiol Care Sci & Soc, Alfred Nobels Alle 23,D3, S-14152 Huddinge, Sweden.
Karolinska Inst, Dept Clin Neurosci, Tomtebodavagen 18A P1 5, S-17177 Stockholm, Sweden.
KTH Royal Inst Technol, Dept Biomed Engn & Hlth Syst, Halsovagen 11, S-14157 Huddinge, Sweden.
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2019 (English)In: MEDICAL IMAGING 2019: IMAGE PROCESSING / [ed] Angelini, ED Landman, BA, SPIE-INT SOC OPTICAL ENGINEERING , 2019, article id 109492OConference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
SPIE-INT SOC OPTICAL ENGINEERING , 2019. article id 109492O
Series
Proceedings of SPIE, ISSN 0277-786X, E-ISSN 1996-756X ; 10949
Keywords [en]
brain MRI, image segmentation, shimming, random forest, statistical shape model
National Category
Medical Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-394733DOI: 10.1117/12.2512409ISI: 000483012700090ISBN: 978-1-5106-2546-4 (electronic)ISBN: 978-1-5106-2545-7 (print)OAI: oai:DiVA.org:uu-394733DiVA, id: diva2:1359543
Conference
Conference on Medical Imaging: Image Processing, FEB 19-21, 2019, San Diego, CA
Available from: 2019-10-09 Created: 2019-10-09 Last updated: 2019-10-09Bibliographically approved

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Andersson, Leif

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