Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Sjölund, Jens; Järlideni, Andreas Eriksson; Andersson, Mats; Knutsson, Hans; Nordström, Håkan

doi:10.1109/icpr.2014.564

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…Possibly, one could use the standard deviation of estimates to identify regions requiring further analysis. This could be done using a machine learning algorithm that also takes the MR, or some features derived thereof, as input (Hofmann et al 2008, Sjölund et al 2014.…”

Section: Discussionmentioning

confidence: 99%

“…Data-based approaches directly associate intensities in the MR images with the Hounsfield units of a CT, either by segmentation and subsequent density assignment (Hsu, Cao, Huang, Feng & Balter 2013, Sjölund et al 2014 or by direct regression (Kapanen & Tenhunen 2013). This is complicated by the fact that MR intensitiesunlike CT-are not calibrated and some type of intensity normalization, also known as bias-field correction, is often needed as a preprocessing step.…”

Section: Introductionmentioning

confidence: 99%

“…The second application is in certain types of radiotherapy planning, where CT's limited soft tissue contrast stunts accurate tumor delineation whereas MR provides valuable guidance. Such cases include stereotactic radiosurgery (Sjölund, Eriksson Järliden, Andersson, Knutsson & Nordström 2014) of brain disorders and radiotherapy treatment of prostate cancer (Rasch, Barillot, Remeijer, Touw, van Herk & Lebesque 1999).…”

Section: Introductionmentioning

confidence: 99%

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Generating patient specific pseudo-CT of the head from MR using atlas-based regression

et al. 2015

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Abstract. Radiotherapy planning and attenuation correction of PET images require simulation of radiation transport. The necessary physical properties are typically derived from computed tomography (CT) images, but in some cases, including stereotactic neurosurgery and combined PET/MR imaging, only magnetic resonance (MR) images are available. With these applications in mind, we describe how a realistic, patient-specific, pseudo-CT of the head can be derived from anatomical MR images. We refer to the method as atlas-based regression, because of its similarity to atlas-based segmentation.Given a target MR and an atlas database comprising MR and CT pairs, atlas-based regression works by registering each atlas MR to the target MR, applying the resulting displacement fields to the corresponding atlas CTs and, finally, fusing the deformed atlas CTs into a single pseudo-CT.We use a deformable registration algorithm known as the Morphon and augment it with a certainty mask that allows a tailoring of the influence certain regions are allowed to have on the registration. Moreover, we propose a novel method of fusion, wherein the collection of deformed CTs is iteratively registered to their joint mean, and find that the resulting mean CT becomes more similar to the target CT. However, the voxelwise median provided even better results; at least as good as earlier work that required special MR imaging techniques. This makes atlas-based regression a good candidate for clinical use.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Generating patient specific pseudo-CT of the head from MR using atlas-based regression

et al. 2015

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“…1a). Then, the skull masks are obtained from CT images, whose voxel values are greater than 300 HU [Sjolund et al, 2014] (Fig. 1b).…”

Section: Semi-manual Segmentations and Semi-manual Atlasesmentioning

confidence: 99%

Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

et al. 2016

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Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of brain tissues. None of the three provide explicit skull/CSF boundary (SCB) since it is challenging to distinguish these dark structures in a T1-weighted image. However, explicit SCB not only leads to a natural way of obtaining TICV (i.e., counting voxels inside the skull) but also allows sub-definition of TICV, e.g., the posterior fossa volume (PFV). In this paper, we propose to use multi-atlas label fusion to obtain TICV and PFV simultaneously. The main contributions are: (1) TICV and PFV are simultaneously obtained with explicit SCB from a single T1-weighted image. (2) TICV and PFV labels are added to the widely used BrainCOLOR atlases. (3) Detailed mathematical derivation of non-local spatial STAPLE (NLSS) label fusion is presented. As the skull is clearly distinguished in CT images, we use a semi-manual procedure to obtain atlases with TICV and PFV labels using 20 subjects who both have a MR and CT scan. The proposed method provides simultaneous TICV and PFV estimation while achieving more accurate TICV estimation compared with FreeSurfer, FSL, SPM12 and the previously proposed STAPLE based approach. The newly developed TICV and PFV labels for the OASIS BrainCOLOR atlases provide acceptable performance, which enables simultaneous TICV and PFV estimation during whole brain segmentation. The NLSS method and the new atlases have been made freely available.

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“…For scalp and skull segmentation from MRI, previous works in the literature have proposed the morphology method, machine‐learning‐based methods (Neural Network, SVM) and atlas‐based methods . Moreover, information from CT data can be used for segmentation.…”

Section: Introductionmentioning

confidence: 99%

3D brain magnetic resonance imaging segmentation by using bitplane and adaptive fast marching

Tuan

Kim

Bao

2018

Int J Imaging Syst Tech

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Diagnosis using medical images helps doctors detect diseases and treat patients effectively. A system that segments objects automatically from magnetic resonance imaging (MRI) plays an important role when doctors diagnose injuries and brain diseases. This article presents a method for automatic brain, scalp, and skull segmentation from MRI that uses Bitplane and the Adaptive Fast Marching method (FMM). We focus on the segmentation of these tissues, especially the brain, because they are the essential objects, and their segmentation is the first step in the segmentation of other tissues. First, the type of each slice is set based on the shape of the brain, and the head region is segmented by removing its background. Second, the sure region and the unsure region are segmented based on the Bitplane method. Finally, this work proposes an approach for classification that is based on the Adaptive FMM. This approach is evaluated with the BrainWeb and Neurodevelopmental MRI databases and compared with other methods. The Dice Averages for brain, scalp, and skull segmentation are 96%, 80%, and 93%, respectively, on the BrainWeb database and 91%, 67%, and 80%, respectively, on the Neurodevelopmental MRI database.

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Skull Segmentation in MRI by a Support Vector Machine Combining Local and Global Features

Cited by 8 publications

References 16 publications

Generating patient specific pseudo-CT of the head from MR using atlas-based regression

Generating patient specific pseudo-CT of the head from MR using atlas-based regression

Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

3D brain magnetic resonance imaging segmentation by using bitplane and adaptive fast marching

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