Fully automated tissue classifier for contrast-enhanced CT scans of adult and pediatric patients.

To develop a consistent, fully-automated classifier for all tissues within the trunk and to more accurately discriminate between tissues (such as bone) and contrast medium with overlapping high CT numbers. Twenty-eight contrast enhanced NCAP (neck-chest-abdomen-pelvis) CT scans (four adult and three pediatric patients) were used to train and test a tissue classification pipeline. The classifier output consisted of six tissue classes: lung, fat, muscle, solid organ, blood/bowel contrast and bone. The input features for training were selected from 28 2D image filters and 12 3D filters, and one hand crafted spatial feature. To improve differentiation between tissue and blood/bowel contrast classification, 70 additional CT images were manually classified. Two different training data sets consisting of manually classified tissues from different locations in body were used to train the models. Training used the random forest algorithm in WEKA (Waikato Environment for Knowledge Analysis); the number of trees was optimized for best out-of-bag error. Automated classification accuracy was compared with manual classification by calculating dice similarity coefficient (DSC). Model performance was tested on 21 manually classified slices (two adult and one pediatric patient). The overall DSC at image locations represented in the training dataset were-lung: 0.98, fat: 0.90, muscle: 0.85, solid organ: 0.75, blood/contrast: 0.82, and bone: 0.90. The overall DSC for slice locations that were not represented in the training dataset were-lung: 0.97, fat: 0.89, muscle: 0.76, solid organ: 0.79, blood: 0.56, and bone: 0.74. Analyzing the classification maps for the entire scan volume revealed that except for misclassifications in the trabecular bone region of the spinal column, and solid organ and blood/contrast interfaces within the abdomen, the results were acceptable. A fully-automated whole-body tissue classifier for adult and pediatric contrast-enhanced CT using random forest algorithm and intensity-based image filters was developed.