title: Segmentation and quantification of intra-ventricular/cerebral  hemorrhage in CT scans by modified distance regularized  level set evolution technique.
abstract: OBJECTIVE
  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.
authors.affiliation: Biomedical Imaging Lab, SBIC, Biopolis, Agency for Science, Technology and Research, #07-01, Matrix, 30, Biopolis Road, Singapore 138671, Singapore. bhanu@sbic.a-star.edu.sg
authors.forename: K N Bhanu
authors.initials: KN
authors.lastname: Prakash
authors.forename: Shi
authors.initials: S
authors.lastname: Zhou
authors.forename: Tim C
authors.initials: TC
authors.lastname: Morgan
authors.forename: Daniel F
authors.initials: DF
authors.lastname: Hanley
authors.forename: Wieslaw L
authors.initials: WL
authors.lastname: Nowinski
mesh_terms: D000465:Algorithms; D002543:Cerebral Hemorrhage / Q000000981:diagnostic imaging*; D014695:Cerebral Ventriculography; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D016032:Randomized Controlled Trials as Topic; D014057:Tomography, X-Ray Computed