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  • 1.
    Bendazzoli, Simone
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    Brusini, Irene
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging. Karolinska Inst, Dept Neurobiol Care Sci & Soc, Alfred Nobels Alle 23,D3, S-14152 Huddinge, Sweden..
    Damberg, Peter
    Karolinska Inst, Dept Clin Neurosci, Tomtebodavagen 18A P1 5, S-17177 Stockholm, Sweden..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Andersson, Leif
    Uppsala Univ, Dept Med Biochem & Microbiol, Sci Life Lab Uppsala, Biomedicinskt Ctr BMC, Husargatan 3, S-75237 Uppsala, Sweden..
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Automatic rat brain segmentation from MRI using statistical shape models and random forest2019In: MEDICAL IMAGING 2019: IMAGE PROCESSING / [ed] Angelini, ED Landman, BA, SPIE-INT SOC OPTICAL ENGINEERING , 2019, article id 1094920Conference 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.

  • 2.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Carneiro, Miguel
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;Univ Porto, Dept Biol, Fac Ciencias, P-4169007 Porto, Portugal..
    Wang, Chunliang
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Rubin, Carl-Johan
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden..
    Ring, Henrik
    Uppsala Univ, Dept Neurosci, S-75236 Uppsala, Sweden..
    Afonso, Sandra
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal..
    Blanco-Aguiar, Jose A.
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;CSIC, Inst Invest Recursos Cineget IREC, Ciudad Real 13005, Spain.;UCLM, CSIC, Ciudad Real 13005, Spain..
    Ferrand, Nuno
    Univ Porto, Ctr Invest Biodiversidade & Recursos Genet CIBIO, InBIO, P-4485661 Vairao, Portugal.;Univ Porto, Dept Biol, Fac Ciencias, P-4169007 Porto, Portugal.;Univ Johannesburg, Dept Zool, ZA-2006 Auckland Pk, South Africa..
    Rafati, Nima
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden..
    Villafuerte, Rafael
    CSIC, IESA, Cordoba 14004, Spain..
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Damberg, Peter
    Karolinska Univ Hosp, Karolinska Expt Res & Imaging Ctr, S-17176 Solna, Sweden..
    Hallbook, Finn
    Uppsala Univ, Dept Neurosci, S-75236 Uppsala, Sweden..
    Fredrikson, Mats
    Uppsala Univ, Dept Psychol, S-75236 Uppsala, Sweden.;Karolinska Inst, Dept Clin Neurosci, S-17177 Stockholm, Sweden..
    Andersson, Leif
    Uppsala Univ, Sci Life Lab Uppsala, Dept Med Biochem & Microbiol, S-75236 Uppsala, Sweden.;Texas A&M Univ, Coll Vet Med & Biomed Sci, Dept Vet Integrat Biosci, College Stn, TX 77843 USA.;Swedish Univ Agr Sci, Dept Anim Breeding & Genet, S-75007 Uppsala, Sweden..
    Changes in brain architecture are consistent with altered fear processing in domestic rabbits2018In: Proceedings of the National Academy of Sciences of the United States of America, ISSN 0027-8424, E-ISSN 1091-6490, Vol. 115, no 28, p. 7380-7385Article in journal (Refereed)
    Abstract [en]

    The most characteristic feature of domestic animals is their change in behavior associated with selection for tameness. Here we show, using high-resolution brain magnetic resonance imaging in wild and domestic rabbits, that domestication reduced amygdala volume and enlarged medial prefrontal cortex volume, supporting that areas driving fear have lost volume while areas modulating negative affect have gained volume during domestication. In contrast to the localized gray matter alterations, white matter anisotropy was reduced in the corona radiata, corpus callosum, and the subcortical white matter. This suggests a compromised white matter structural integrity in projection and association fibers affecting both afferent and efferent neural flow, consistent with reduced neural processing. We propose that compared with their wild ancestors, domestic rabbits are less fearful and have an attenuated flight response because of these changes in brain architecture.

  • 3.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Jörgens, Daniel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Dependency of neural tracts'€™ curvature estimations on tractography methods2017Conference paper (Refereed)
  • 4.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Jörgens, Daniel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Influence of Tractography Algorithms and Settings on Local Curvature Estimations2017Conference paper (Refereed)
  • 5.
    Brusini, Irene
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Jörgens, Daniel
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Smedby, Örjan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Moreno, Rodrigo
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Medical Imaging.
    Voxel-Wise Clustering of Tractography Data for Building Atlases of Local Fiber Geometry2019Conference paper (Refereed)
    Abstract [en]

    This paper aims at proposing a method to generate atlases of white matter fibers’ geometry that consider local orientation and curvature of fibers extracted from tractography data. Tractography was performed on diffusion magnetic resonance images from a set of healthy subjects and each tract was characterized voxel-wise by its curvature and Frenet–Serret frame, based on which similar tracts could be clustered separately for each voxel and each subject. Finally, the centroids of the clusters identified in all subjects were clustered to create the final atlas. The proposed clustering technique showed promising results in identifying voxel-wise distributions of curvature and orientation. Two tractography algorithms (one deterministic and one probabilistic) were tested for the present work, obtaining two different atlases. A high agreement between the two atlases was found in several brain regions. This suggests that more advanced tractography methods might only be required for some specific regions in the brain. In addition, the probabilistic approach resulted in the identification of a higher number of fiber orientations in various white matter areas, suggesting it to be more adequate for investigating complex fiber configurations in the proposed framework as compared to deterministic tractography.

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