About Segmath, a new Cerebral Vascular Segmentation Software after CTA

Main Article Content

Daniel Violon*

Abstract



Objectives: The new segmentation software Segmath delivers a 3D view of the cerebral vascular structures without superposition of bony or other structures. This will, according to the literature, improve the workflow of stroke patients and increase the occlusion detection rate on the original CTA.


Materials and methods: The software written in MATLAB is based on the analysis of the local Hessian matrix with new original functions of the resulting local eigenvalues. No user intervention in the segmentation process is needed.


Results: The validation of the new software yields good results both with synthetic data and real CTA’s.


Conclusion: This segmentation software is a powerful additional diagnostic tool available to radiologists and neurologists examining and treating stroke patients. This will improve the workflow of suspected stroke patients.



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Article Details

Violon, D. (2022). About Segmath, a new Cerebral Vascular Segmentation Software after CTA. Trends in Computer Science and Information Technology, 7(3), 094–098. https://doi.org/10.17352/tcsit.000057
Research Articles

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Worldhealthrankings Live Longer Live Better. https://www.worldlifeexpectancy.com/belgium-stroke

The Burden of Stroke in Belgium. https://www.safestroke.eu/wp-content/uploads/2017/12/SAFE_STROKE_BELGIUM.pdf.

http://www.emro.who.int/health-topics/stroke-cerebrovascular-accident/index.html.

Deshpande A, Jamilpour N, Jiang B, Michel P, Eskandari A, Kidwell C, Wintermark M, Laksari K. Automatic segmentation, feature extraction and comparison of healthy and stroke cerebral vasculature. Neuroimage Clin. 2021;30:102573. doi: 10.1016/j.nicl.2021.102573. Epub 2021 Jan 26. PMID: 33578323; PMCID: PMC7875826.

Fasen BACM, Heijboer RJJ, Hulsmans FH, Kwee RM. CT Angiography in Evaluating Large-Vessel Occlusion in Acute Anterior Circulation Ischemic Stroke: Factors Associated with Diagnostic Error in Clinical Practice. AJNR Am J Neuroradiol. 2020 Apr;41(4):607-611. doi: 10.3174/ajnr.A6469. Epub 2020 Mar 12. PMID: 32165362; PMCID: PMC7144646.

Fu F, Wei J, Zhang M, Yu F, Xiao Y, Rong D, Shan Y, Li Y, Zhao C, Liao F, Yang Z, Li Y, Chen Y, Wang X, Lu J. Rapid vessel segmentation and reconstruction of head and neck angiograms using 3D convolutional neural network. Nat Commun. 2020 Sep 24;11(1):4829. doi: 10.1038/s41467-020-18606-2. PMID: 32973154; PMCID: PMC7518426.

MATLAB. version 7.10.0 (R2010a). Natick MTMI. 2010.

Frangi A, Niessen W, Vincken K. Multiscale vessel enhancement filtering. Medical Image Computing and Computer-Assisted Intervention. 1998; 1496:130-137.

Araùjo R, Cardoso J, Oliveira H. Pattern Recognition and Image Analysis.Deep Vesselness Measure from Scale-Space Analysis of hessian Matrix Eigenvalues; 2019.

Kamath S. Observations on the length and diameter of vessels forming the circle of Willis. J Anat. 1981 Oct;133(Pt 3):419-23. PMID: 7328048; PMCID: PMC1167613.

Manniesing R, Viergever MA, Niessen WJ. Vessel enhancing diffusion: a scale space representation of vessel structures. Med Image Anal. 2006 Dec;10(6):815-25. doi: 10.1016/j.media.2006.06.003. Epub 2006 Jul 28. PMID: 16876462.

Koenderink JJ. The structure of images. Biol Cybern. 1984;50(5):363-70. doi: 10.1007/BF00336961. PMID: 6477978.

Cody DD. AAPM/RSNA physics tutorial for residents: topics in CT. Image processing in CT. Radiographics. 2002 Sep-Oct;22(5):1255-68. doi: 10.1148/radiographics.22.5.g02se041255. PMID: 12235351.

Dice L. Measures of the Amount of Ecologic association between Species. Ecology. 1945; 26(3):297-302.

Shamir R, Duchin Y, Kim J. Continuous Dice Coefficient: a method for Evaluating Probabilistic Segmentations. DOIhttps://doi.org/10.1101/306977.

Jin M, Hao D, Ding S, Qin B. Low-rank and sparse decomposition with spatially adaptive filtering for sequential segmentation of 2D+t vessels. Phys Med Biol. 2018 Aug 29;63(17):17LT01. doi: 10.1088/1361-6560/aad8e0. PMID: 30088812.

Jerman T, Pernus F, Likar B, Spiclin Z. Enhancement of Vascular Structures in 3D and 2D Angiographic Images. IEEE Trans Med Imaging. 2016 Sep;35(9):2107-2118. doi: 10.1109/TMI.2016.2550102. Epub 2016 Apr 4. PMID: 27076353.

Sankaran S, Schaap M, Hunley S. Healthy Area of Lumen Estimation for Vessel Stenosis Quantification. In International Conference on Medical Image Computing and Computer-Assisted Intervention.2016;380-387.

Muzzolini R, Pierson R, Yang YH.Three Dimensional Segmentation of Volume Data. In Proceedings of 1st International Conference on Image Processing. 1994;13-16 DOI: 10.1109/ICIP.1994.413758.

Moccia S, De Momi E, El Hadji S, Mattos LS. Blood vessel segmentation algorithms - Review of methods, datasets and evaluation metrics. Comput Methods Programs Biomed. 2018 May;158:71-91. doi: 10.1016/j.cmpb.2018.02.001. Epub 2018 Feb 10. PMID: 29544791.

Manniesing R, Viergever MA, van der Lugt A, Niessen WJ. Cerebral arteries: fully automated segmentation from CT angiography--a feasibility study. Radiology. 2008 Jun;247(3):841-6. doi: 10.1148/radiol.2473070436. PMID: 18487538.

Hladuvka J, Gröller E. Exploiting the Hessian Matrix for Content-Based Retrieval of Volume-Data Features.The Visual Computer. 2002; 18:207-217 doi: 10.1007/s003710100141.

Dzyubak OP, Ritman EL. Automation of Hessian-Based Tubularity Measure Response Function in 3D Biomedical Images. Int J Biomed Imaging. 2011;2011:920401. doi: 10.1155/2011/920401. Epub 2011 Feb 22. PMID: 21437202; PMCID: PMC3062949.

Stefani M, Schneider F, Marrone A. Influence of the Gender on Cerebral Vascular Diameters Observed during the Magnetic Resonance Angiographic Examination of the Willis Circle. Brazilian Archives of Biology and Technology. 2013; 56(1):45-52.

Florack L, ter Haar Romeny B, Koenderink J. Scale and the Differential Structure of Images. Image and Vision Computing. 1992; 10(6):376-388.

Li H, Yezzi A. Vessels as 4-D curves: global minimal 4-D paths to extract 3-D tubular surfaces and centerlines. IEEE Trans Med Imaging. 2007 Sep;26(9):1213-23. doi: 10.1109/tmi.2007.903696. PMID: 17896594.

Firouzian A, Manniesing R, Flach ZH, Risselada R, van Kooten F, Sturkenboom MC, van der Lugt A, Niessen WJ. Intracranial aneurysm segmentation in 3D CT angiography: method and quantitative validation with and without prior noise filtering. Eur J Radiol. 2011 Aug;79(2):299-304. doi: 10.1016/j.ejrad.2010.02.015. Epub 2010 Mar 25. PMID: 20346606.

Mendrik A, Vonken EJ, van Ginneken B, Smit E, Waaije A, Bertolini G, Viergever MA, Prokop M. Automatic segmentation of intracranial arteries and veins in four-dimensional cerebral CT perfusion scans. Med Phys. 2010 Jun;37(6):2956-66. doi: 10.1118/1.3397813. PMID: 20632608.

Santos EM, Marquering HA, Berkhemer OA, van Zwam WH, van der Lugt A, Majoie CB, Niessen WJ; MR CLEAN investigators. Development and validation of intracranial thrombus segmentation on CT angiography in patients with acute ischemic stroke. PLoS One. 2014 Jul 17;9(7):e101985. doi: 10.1371/journal.pone.0101985. PMID: 25032691; PMCID: PMC4102487.

Kunz WG, Sommer WH, Havla L, Dorn F, Meinel FG, Dietrich O, Buchholz G, Ertl-Wagner B, Thierfelder KM. Detection of single-phase CTA occult vessel occlusions in acute ischemic stroke using CT perfusion-based wavelet-transformed angiography. Eur Radiol. 2017 Jun;27(6):2657-2664. doi: 10.1007/s00330-016-4613-y. Epub 2016 Oct 8. PMID: 27722798.

Meijs M, Patel A, van de Leemput SC, Prokop M, van Dijk EJ, de Leeuw FE, Meijer FJA, van Ginneken B, Manniesing R. Robust Segmentation of the Full Cerebral Vasculature in 4D CT of Suspected Stroke Patients. Sci Rep. 2017 Nov 15;7(1):15622. doi: 10.1038/s41598-017-15617-w. PMID: 29142240; PMCID: PMC5688074.

Ronchetti T, Jud C, Maloca PM, Orgül S, Giger AT, Meier C, Scholl HPN, Chun RKM, Liu Q, To CH, Považay B, Cattin PC. Statistical framework for validation without ground truth of choroidal thickness changes detection. PLoS One. 2019 Jun 28;14(6):e0218776. doi: 10.1371/journal.pone.0218776. PMID: 31251762; PMCID: PMC6599222.

Williams GW. Comparing the joint agreement of several raters with another rater. Biometrics. 1976 Sep;32(3):619-27. PMID: 963175.

Pinto A, Brunese L. Spectrum of diagnostic errors in radiology. World J Radiol. 2010 Oct 28;2(10):377-83. doi: 10.4329/wjr.v2.i10.377. PMID: 21161023; PMCID: PMC2999012.

Amukotuwa SA, Straka M, Dehkharghani S, Bammer R. Fast Automatic Detection of Large Vessel Occlusions on CT Angiography. Stroke. 2019 Dec;50(12):3431-3438. doi: 10.1161/STROKEAHA.119.027076. Epub 2019 Nov 4. PMID: 31679501; PMCID: PMC6878187.

Sahin H, Pekcevic Y. Anatomical Variations of the Circle of Willis: Evaluation with CT Angiography. Anatomy.12(1): 20-26 doi:10.2399/ana.18.003.