Abstract
Purpose
An automatic, accurate and fast segmentation of hemorrhage in brain Computed Tomography (CT) images is necessary for quantification and treatment planning when assessing a large number of data sets. Though manual segmentation is accurate, it is time consuming and tedious. Semi-automatic methods need user interactions and might introduce variability in results. Our study proposes a modified distance regularized level set evolution (MDRLSE) algorithm for hemorrhage segmentation.
Methods
Study data set (from the ongoing CLEAR-IVH phase III clinical trial) is comprised of 200 sequential CT scans of 40 patients collected at 10 different hospitals using different machines/vendors. Data set contained both constant and variable slice thickness scans. Our study included pre-processing (filtering and skull removal), segmentation (MDRLSE which is a two-stage method with shrinking and expansion) with modified parameters for faster convergence and higher accuracy and post-processing (reduction in false positives and false negatives).
Results
Results are validated against the gold standard marked manually by a trained CT reader and neurologist. Data sets are grouped as small, medium and large based on the volume of blood. Statistical analysis is performed for both training and test data sets in each group. The median Dice statistical indices (DSI) for the 3 groups are 0.8971, 0.8580 and 0.9173 respectively. Pre- and post-processing enhanced the DSI by 8 and 4% respectively.
Conclusions
The MDRLSE improved the accuracy and speed for segmentation and calculation of the hemorrhage volume compared to the original DRLSE method. The method generates quantitative information, which is useful for specific decision making and reduces the time needed for the clinicians to localize and segment the hemorrhagic regions.
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Nieuwkamp DJ, de Gans K, Rinkel GJ et al (2000) Treatment and outcome of severe intraventricular extension in patients with subarachnoid or intracerebral hemorrhage: a systematic review of the literature. J Neurol 247(2): 117–121
Davis SM, Broderick J, Hennerici M et al (2006) Hematoma growth is a determinant of mortality and poor outcome after intracerebral hemorrhage. Neurology 66(8): 1175–1181
Flaherty ML, Haverbusch M, Sekar P et al (2006) Long-term mortality after intracerebral hemorrhage. Neurology 66(8): 1182–1186
http://www.ccmtutorials.com/neuro/. Accessed on 20 Sept 2011
Chen W, Smith R, Ji SY et al (2009) Automated ventricular systems segmentation in brain CT images by combining low-level segmentation and high-level template matching. BMC Med Inform Decis Mak 9(Suppl 1): S4
Cosic D, Loncaric S (1997) Computer system for quantitative analysis of ICH from CT head images. In: Proceedings of the 19th international conference on IEEE/EMBS, pp 553–556
Cheng D, Cheng K (1998) A PC-based medical image analysis system for brain CT hemorrhage area extraction. In: Proceedings of the 11th IEEE symposium on computer-based medical systems, p 240
Loncaric S, Dhawan AP, Kovacevic D, et al (1999) Quantitative intracerebral brain hemorrhage analysis. In: Proceedings of the SPIE medical imaging, vol 3661, pp 886–941
Perez N, Valdez JA, Guevara MA, et al (2007) Set of methods for spontaneous ICH segmentation and tracking from CT head images. In: Proceedings of the 12th Iberoamerican congress on pattern recognition (CIARP), vol 4756, pp 212–220
Chan T. (2007) Computer aided detection of small acute intracranial hemorrhage on computer tomography of brain. Comput Med Imaging Graph 31(4–5): 285–298
Yuh EL, Gean AD, Manley GT et al (2008) Computer-aided assessment of head computed tomography (CT) studies in patients with suspected traumatic brain injury. J Neurotrauma 25(10): 1163–1172
Liu B, Yuan Q, Liu Z et al (2008) Automatic segmentation of intracranial hematoma and volume measurement. Conf Proc IEEE Eng Med Biol Soc 2008: 1214–1217
Bardera A, Boada I, Feixas M et al (2009) Semi-automated method for brain hematoma and edema quantification using computed tomography. Comput Med Imaging Graph 33(4): 304–311
Kass M, Witkin A, Terzopoulos D (1987) Snakes: active contour models. Int J Comput Vis 1: 321–331
Osher S, Sethian J (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79(1): 12–49
Gomes J, Faugeras O (2000) Reconciling distance functions and level sets. J Vis Commun Image Represent 11(2): 209–223
Li C, Xu C, Gui C, Fox MD (2005) Level set evolution without re-initialization: A variational formulation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, vol 1, pp 430–436
Li C, Xu C, Gui C, Fox M (2010) Distance regularized level set evolution and its application to image segmentation. IEEE Trans Image Process 19(12): 3243–3254
Nyquist P, Hanley DF (2007) The use of intraventricular thrombolytics in intraventricular hemorrhage. J Neurol Sci 261(1–2): 84–88
Morgan T, Awad I, Keyl P et al (2008) Preliminary report of the clot lysis evaluating accelerated resolution of intraventricular hemorrhage (CLEAR-IVH) clinical trial. Acta Neurochir Suppl 105: 217–220
CLEAR III Study Group. Ongoing phase III clinical trial. http://www.cleariii.com
Fan J, Zeng F, Body M et al (2005) Seeded region growing: an extensive and comparative study. Pattern Recognit Lett 2005(26): 1139–1156
Zou KH, Warfield SK, Bharatha A et al (2004) Statistical validation of image segmentation quality based on a spatial overlap index. Acad Radiol 11(2): 178–189
Chang H-H, Zhuang A, Valentino D, Chu W-C (2009) Performance measure characterization for evaluating neuroimage segmentation algorithms. Neuroimage 47(1): 122–135
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Prakash, K.N.B., Zhou, S., Morgan, T.C. et al. Segmentation and quantification of intra-ventricular/cerebral hemorrhage in CT scans by modified distance regularized level set evolution technique. Int J CARS 7, 785–798 (2012). https://doi.org/10.1007/s11548-012-0670-0
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DOI: https://doi.org/10.1007/s11548-012-0670-0