Name: automated midline shift and intracranial pressure estimation based on brain ct images
Uploaded: 2013-04-13T14:00:00
Description: Watch this Scientific Journal Video about Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images at JoVE.com

The material is based upon work partially supported by the National Science Foundation under Grant No. IIS0758410. The data was supplied by Carolinas Healthcare System.

1. Methodology Overview
The proposed framework processes the brain CT images of traumatic brain injury (TBI) patients to automatically calculate midline shift in pathological cases and use it as well as other extracted information to predict intracranial pressure (ICP). Figure 1 shows the schematic diagram of the entire framework. The automated midline shift measurement can be divided into three steps. First, the ideal midline of the brain, i.e. the midline before injur...

<ol>
	<li>Langlois, J. A., Rutland-Brown, W., Thomas, K. E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Traumatic brain injury in the united states: emergency department visits, hospitalizations, and deaths. , (2006).</li><li>Moore, E. E., Feliciano, D. V., Mattox, K. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Trauma. , (2003).</li><li>Chen, W., Smith, R., Ji, S. Y., Ward, K. R., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+Ventricular+Systems+Segmentation+in+Brain+CT+Images+by+Combining+Low-level+Segmentation+and+High-level+Template+Matching.">Automated Ventricular Systems Segmentation in Brain CT Images by Combining Low-level Segmentation and High-level Template Matching.</a> BMC Medical Informatics and Decision Making. 9, (2009).</li><li>Chen, W., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Segmentation+of+Ventricles+in+Brain+CT+Images+Using+Gaussian+Mixture+Model+Method.">Segmentation of Ventricles in Brain CT Images Using Gaussian Mixture Model Method.</a> , 15-20 (2009).</li><li>Chen, W., Ward, K. R., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Actual+Midline+Estimation+from+Brain+CT+Scan+Using+Multiple+Regions+Shape+Matching.">Actual Midline Estimation from Brain CT Scan Using Multiple Regions Shape Matching.</a> , 2552-2555 (2010).</li><li>Chen, W., Cockrell, C., Ward, K. R., Najarian, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Intracranial+Pressure+Level+Prediction+in+Traumatic+Brain+Injury+by+Extracting+Features+from+Multiple+Sources+and+Using+Machine+Learning+Methods.">Intracranial Pressure Level Prediction in Traumatic Brain Injury by Extracting Features from Multiple Sources and Using Machine Learning Methods.</a> , 510-515 (2010).</li><li>Greenspan, H., Ruf, A., Goldberger, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Constrained+Gaussian+mixture+model+framework+for+automatic+segmentation+of+MR+brain+images.">Constrained Gaussian mixture model framework for automatic segmentation of MR brain images.</a> IEEE Trans. Med. Imaging. 25 (9), 1233-1245 (2006).</li><li>Belongie, S., Malik, J., Puzicha, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Shape+matching+and+object+recognition+using+shape+contexts.">Shape matching and object recognition using shape contexts.</a> IEEE Trans. Pattern Anal. Mach. Intell. 24, 509-522 (2002).</li><li>Weszaka, J. S., Dyer, C. R., Rosenfeld, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+comparative+study+of+texture+measures+for+terrain+classification.">A comparative study of texture measures for terrain classification.</a> IEEE Trans. on Syst., Man, Cyber. , (1976).</li><li>Kingsbury, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Complex+wavelets+for+shift+invariant+analysis+and+filtering+of+signals.">Complex wavelets for shift invariant analysis and filtering of signals.</a> Applied and Computational Harmonic Analysis. 10 (3), 234-253 (2002).</li><li>Mierswa, I., Wurst, M., Klinkenberg, R., Scholz, M., Euler, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=+Yale:+Rapid+prototyping+for+complex+data+mining+tasks."> Yale: Rapid prototyping for complex data mining tasks.</a> , 935-940 (2006).</li></ol>

In this study, an intuitive and flexible framework is proposed to address two challenging problems: the estimation of the midline shift in CT images and ICP level prediction based on extracted features. The evaluation results show the effectiveness of the proposed method. As far as we know, this is the first time of a systematic study in addressing these two problems. We notice that based on the general framework, there are many potential improvements that can be achieved. For example, in the proposed segmentation, the l...

Every year there are about 1.4 million traumatic brain injuries (TBI) related emergency department cases in the United States, of which, over 50,000 result in death1. Severe TBI is usually accompanied by an increase in intracranial pressure (ICP) with symptoms such as hematomas and swelling brain tissue. These result in reduced cerebral perfusion pressure and cerebral blood flow, placing the injured brain in additional risk. Severe ICP increase can be fatal, so monitoring ICP for patients with TBI is crucial. This typically requires placement of indwelling catheters directly into the brain for monitoring of pressure, a risky procedure for patients that can only be performed at specialized medical centers. The procedure also involves risk such as infection. However, some signs of elevated ICP may be noticeable in medical imaging. In particular, midline shift is often associated with an increase in the ICP and can be captured from the brain computed tomography (CT) images. As such, these images provide an opportunity for non-invasive detection of elevated ICP which can be used as a pre-screening step before cranial trepanation. CT imaging is still the gold standard for initial TBI assessment among all other imaging modalities, e.g. MRI, because of its high speed and relative low cost2. Furthermore, a CT examination does not require strict patient immobility, and has advantage in revealing severe abnormalities such as bone fractures and hematomas. While CT is commonly used for detection of injuries in the brain, based on the current technology, midline shift is not automatically measured and therefore physicians must assess this important factor by visual inspection. Inaccurate or inconsistent CT interpretation is often associated with the nature of the human visual system and the complex structure of the brain. While small midline shifts are elusive, they are often invaluable for assessment of brain injury, in particular at early stages of injury before a patient's condition becomes more severe. On the other side of the spectrum, large midline shift suggests highly elevated ICP and more severe TBI. However, it is a very challenging task for humans to visually inspect CT images and predict the level of ICP quantitatively. Due to advances in automated computational techniques, features extracted from CT images, such as midline shift, hematoma volume, and texture of brain CT images, can be measured accurately and automatically using advanced image processing methods. However, the relationship between ICP and midline shift as well as other features such as degree of bleeding, the texture from CT images has not been explored. In this paper, a computational framework has been proposed to measure the midline shift measurement as well as other physiological / anatomical features on brain CT images and then predict the degree of ICP non-intrusively using machine learning techniques.

automated midline shift and intracranial pressure estimation based on brain ct images

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift estimation and intracranial pressure (ICP) pre-screening system. To estimate the midline shift, first an estimation of the ideal midline is performed based on the symmetry of the skull and anatomical features in the brain CT scan. Then, segmentation of the ventricles from the CT scan is performed and used as a guide for the identification of the actual midline through shape matching. These processes mimic the measuring process by physicians and have shown promising results in the evaluation. In the second component, more features are extracted related to ICP, such as the texture information, blood amount from CT scans and other recorded features, such as age, injury severity score to estimate the ICP are also incorporated. Machine learning techniques including feature selection and classification, such as Support Vector Machines (SVMs), are employed to build the prediction model using RapidMiner. The evaluation of the prediction shows potential usefulness of the model. The estimated ideal midline shift and predicted ICP levels may be used as a fast pre-screening step for physicians to make decisions, so as to recommend for or against invasive ICP monitoring.

The testing CT datasets were provided by the Carolinas Healthcare System (CHS) under Institutional Review Board approval. All subjects were diagnosed with mild to severe TBI when first admitted to hospital. For each patient, the ICP value was recorded every hour using ICP probes inside the ventricle region both before and after CT scans were obtained. To associate the ICP value with each CT scan, average the two closest measurements of ICP to the time of CT scan, both of which are within an hour of the CT scan. Then assi...

Watch this Scientific Journal Video about Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images at JoVE.com

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

An automated midline shift estimation and intracranial pressure (ICP) pre-screening system based on computed tomography (CT) images for patients with traumatic brain injury (TBI) is proposed using image processing and machine learning techniques.

In this paper we present an automated system based mainly on the computed tomography (CT) images consisting of two main components: the midline shift ...

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