PItcHPERFeCT: Primary Intracranial Hemorrhage Probability Estimation using Random Forests on CT Academic Article uri icon


MeSH Major

  • Intracranial Hemorrhages
  • Minimally Invasive Surgical Procedures
  • Outcome Assessment (Health Care)
  • Tissue Plasminogen Activator
  • Tomography, X-Ray Computed


  • The Minimally Invasive Surgery plus rt-PA in ICH Evacuation (MISTIE) trial was a multi-site Phase II clinical trial that tested the safety of hemorrhage removal using recombinant-tissue plasminogen activator (rt-PA). For this analysis, we use 112 baseline CT scans from patients enrolled in the MISTE trial, one CT scan per patient. ICH was manually segmented on these CT scans by expert readers. We derived a set of imaging predictors from each scan. Using 10 randomly-selected scans, we used a first-pass voxel selection procedure based on quantiles of a set of predictors and then built 4 models estimating the voxel-level probability of ICH. The models used were: 1) logistic regression, 2) logistic regression with a penalty on the model parameters using LASSO, 3) a generalized additive model (GAM) and 4) a random forest classifier. The remaining 102 scans were used for model validation.For each validation scan, the model predicted the probability of ICH at each voxel. These voxel-level probabilities were then thresholded to produce binary segmentations of the hemorrhage. These masks were compared to the manual segmentations using the Dice Similarity Index (DSI) and the correlation of hemorrhage volume of between the two segmentations. We tested equality of median DSI using the Kruskal-Wallis test across the 4 models. We tested equality of the median DSI from sets of 2 models using a Wilcoxon signed-rank test.

publication date

  • January 2017



  • Academic Article



  • eng

PubMed Central ID

  • PMC5328741

Digital Object Identifier (DOI)

  • 10.1016/j.nicl.2017.02.007

PubMed ID

  • 28275541

Additional Document Info

start page

  • 379

end page

  • 390


  • 14