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2016 ; 61
(17
): 6553-69
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Tissue segmentation of computed tomography images using a Random Forest
algorithm: a feasibility study
#MMPMID27530679
Polan DF
; Brady SL
; Kaufman RA
Phys Med Biol
2016[Sep]; 61
(17
): 6553-69
PMID27530679
show ga
There is a need for robust, fully automated whole body organ segmentation for
diagnostic CT. This study investigates and optimizes a Random Forest algorithm
for automated organ segmentation; explores the limitations of a Random Forest
algorithm applied to the CT environment; and demonstrates segmentation accuracy
in a feasibility study of pediatric and adult patients. To the best of our
knowledge, this is the first study to investigate a trainable Weka segmentation
(TWS) implementation using Random Forest machine-learning as a means to develop a
fully automated tissue segmentation tool developed specifically for pediatric and
adult examinations in a diagnostic CT environment. Current innovation in computed
tomography (CT) is focused on radiomics, patient-specific radiation dose
calculation, and image quality improvement using iterative reconstruction, all of
which require specific knowledge of tissue and organ systems within a CT image.
The purpose of this study was to develop a fully automated Random Forest
classifier algorithm for segmentation of neck-chest-abdomen-pelvis CT
examinations based on pediatric and adult CT protocols. Seven materials were
classified: background, lung/internal air or gas, fat, muscle, solid organ
parenchyma, blood/contrast enhanced fluid, and bone tissue using Matlab and the
TWS plugin of FIJI. The following classifier feature filters of TWS were
investigated: minimum, maximum, mean, and variance evaluated over a voxel radius
of 2 (n) , (n from 0 to 4), along with noise reduction and edge preserving
filters: Gaussian, bilateral, Kuwahara, and anisotropic diffusion. The Random
Forest algorithm used 200 trees with 2 features randomly selected per node. The
optimized auto-segmentation algorithm resulted in 16 image features including
features derived from maximum, mean, variance Gaussian and Kuwahara filters. Dice
similarity coefficient (DSC) calculations between manually segmented and Random
Forest algorithm segmented images from 21 patient image sections, were analyzed.
The automated algorithm produced segmentation of seven material classes with a
median DSC of 0.86??±??0.03 for pediatric patient protocols, and 0.85??±??0.04
for adult patient protocols. Additionally, 100 randomly selected patient
examinations were segmented and analyzed, and a mean sensitivity of 0.91 (range:
0.82-0.98), specificity of 0.89 (range: 0.70-0.98), and accuracy of 0.90 (range:
0.76-0.98) were demonstrated. In this study, we demonstrate that this fully
automated segmentation tool was able to produce fast and accurate segmentation of
the neck and trunk of the body over a wide range of patient habitus and scan
parameters.
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