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Automated extraction and labelling of the arterial tree from whole-body MRA data.

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Title: Automated extraction and labelling of the arterial tree from whole-body MRA data.
Authors: Shahzad, Rahil1 rahilshahzad@gmail.com, Dzyubachyk, Oleh1, Staring, Marius1, Kullberg, Joel2, Johansson, Lars2, Ahlström, Håkan2, Lelieveldt, Boudewijn P.F.1,3, van der Geest, Rob J.1
Source: Medical Image Analysis. Aug2015, Vol. 24 Issue 1, p28-40. 13p.
Subjects: Whole body imaging, Magnetic resonance angiography, Robust control, Medical registries, Abdominal physiology
Abstract: In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts ( i ) 3D volume reconstruction from different stations with simultaneous correction of different types of intensity inhomogeneity, and ( ii ) Extraction of the arterial tree and subsequent labelling of the pruned extracted tree. Extraction of the arterial tree is performed using the probability map of the “contrast” class, which is obtained as one of the results of the inhomogeneity correction scheme. We demonstrate that such approach is more robust than using the difference between the pre- and post-contrast channels traditionally used for this purpose. Labelling the extracted tree is performed by using a combination of graph-based and atlas-based approaches. Validation of our method with respect to the extracted tree was performed on the arterial tree subdivided into 32 segments, 82.4% of which were completely detected, 11.7% partially detected, and 5.9% were missed on a cohort of 35 subjects. With respect to automated labelling accuracy of the 32 segments, various registration strategies were investigated on a training set consisting of 10 scans. Further analysis on the test set consisting of 25 data sets indicates that 69% of the vessel centerline tree in the head and neck region, 80% in the thorax and abdomen region, and 84% in the legs was accurately labelled to the correct vessel segment. These results indicate clinical potential of our approach in enabling fully automated and accurate analysis of the entire arterial tree. This is the first study that not only automatically extracts the WB-MRA arterial tree, but also labels the vessel tree segments. [ABSTRACT FROM AUTHOR]
Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: Automated extraction and labelling of the arterial tree from whole-body MRA data.
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  Data: <searchLink fieldCode="AR" term="%22Shahzad%2C+Rahil%22">Shahzad, Rahil</searchLink><relatesTo>1</relatesTo><i> rahilshahzad@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Dzyubachyk%2C+Oleh%22">Dzyubachyk, Oleh</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Staring%2C+Marius%22">Staring, Marius</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kullberg%2C+Joel%22">Kullberg, Joel</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Johansson%2C+Lars%22">Johansson, Lars</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Ahlström%2C+Håkan%22">Ahlström, Håkan</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Lelieveldt%2C+Boudewijn+P%2EF%2E%22">Lelieveldt, Boudewijn P.F.</searchLink><relatesTo>1,3</relatesTo><br /><searchLink fieldCode="AR" term="%22van+der+Geest%2C+Rob+J%2E%22">van der Geest, Rob J.</searchLink><relatesTo>1</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Medical+Image+Analysis%22">Medical Image Analysis</searchLink>. Aug2015, Vol. 24 Issue 1, p28-40. 13p.
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  Data: <searchLink fieldCode="DE" term="%22Whole+body+imaging%22">Whole body imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Magnetic+resonance+angiography%22">Magnetic resonance angiography</searchLink><br /><searchLink fieldCode="DE" term="%22Robust+control%22">Robust control</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+registries%22">Medical registries</searchLink><br /><searchLink fieldCode="DE" term="%22Abdominal+physiology%22">Abdominal physiology</searchLink>
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  Data: In this work, we present a fully automated algorithm for extraction of the 3D arterial tree and labelling the tree segments from whole-body magnetic resonance angiography (WB-MRA) sequences. The algorithm developed consists of two core parts ( i ) 3D volume reconstruction from different stations with simultaneous correction of different types of intensity inhomogeneity, and ( ii ) Extraction of the arterial tree and subsequent labelling of the pruned extracted tree. Extraction of the arterial tree is performed using the probability map of the “contrast” class, which is obtained as one of the results of the inhomogeneity correction scheme. We demonstrate that such approach is more robust than using the difference between the pre- and post-contrast channels traditionally used for this purpose. Labelling the extracted tree is performed by using a combination of graph-based and atlas-based approaches. Validation of our method with respect to the extracted tree was performed on the arterial tree subdivided into 32 segments, 82.4% of which were completely detected, 11.7% partially detected, and 5.9% were missed on a cohort of 35 subjects. With respect to automated labelling accuracy of the 32 segments, various registration strategies were investigated on a training set consisting of 10 scans. Further analysis on the test set consisting of 25 data sets indicates that 69% of the vessel centerline tree in the head and neck region, 80% in the thorax and abdomen region, and 84% in the legs was accurately labelled to the correct vessel segment. These results indicate clinical potential of our approach in enabling fully automated and accurate analysis of the entire arterial tree. This is the first study that not only automatically extracts the WB-MRA arterial tree, but also labels the vessel tree segments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
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  Data: <i>Copyright of Medical Image Analysis is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Robust control
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              Text: Aug2015
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