This document summarizes a study that developed a random forest classifier to improve bone tumor segmentation on bone scans. The classifier uses contextual information like offset vectors from tumor landmarks and symmetry features, in addition to intensity and texture features. It was evaluated on bone scans from prostate cancer patients and showed improved segmentation performance over an existing rule-based method, with the contextual features providing most of the discriminative power. The random forest classifier with contextual features increased the Jaccard index by 0.09 compared to a classifier without contexts, and by 0.08 compared to the previous rule-based method, better reducing false positives.

1. Bone tumor segmentation on bone scans using context information
and random forests
Introduction
Email: gregory.h.chu@gmail.com
Motivation:
• Metrics derived from bone tumor segmentation
serve as clinical endpoints in metastatic prostate
cancer clinical drug trials [1]
• The currently used rule-based method for bone
tumor segmentation [2] results in a large # of false
positives (FP). FPs are frequently due to 1) location
dependent tumor appearance, 2) degenerative
joint disease, 3) tracer outside of the bone
Aims:
• To develop a random forest (RF) classifier that uses
contextual information (CLFw context), and evaluate
the significance of the context features by
comparing to a classifier that does not use context
features (CLF w/o context)
• To improve segmentation performance over the
rule-based method [2]
Methods
Experiments and results Conclusion
Gregory Chu1, Pechin Lo1, Bharath Ramakrishna1, Hyun J Kim1, Darren Morris2, Jonathan G. Goldin1, Matthew S. Brown1
1 Center for Computer Vision and Imaging Biomarkers, Department of Radiological Sciences, David Geffen School of Medicine, UCLA, Los Angeles, USA
2 MedQIA Imaging CRO, Los Angeles, USA
Fig. 2: (a) Example result of landmark detection, (b) offset vectors from
point p to all landmark points, (c) probability map of regions prone to
false positives
References
1. Scher, H., et al.: An exploratory analysis of bone scan lesion
area, circulating tumor cell change, pain reduction, and
overall survival in patients with castration-resistant prostate
cancer treated with cabozantinib. J Clin. Onc. 31, 15, 5026
(2013)
2. Chu, G., et al.: Prelimiary results of automated removal of
degenerative joint disease in bone scan lesion segmentation.
Proc. SPIE Medical Imaging, 867007 (2013)
• 213 pairs of anterior (AP) and posterior (PA) bone
scans from 56 sites with metastatic prostate cancer,
with pixel spacing ranging from [2, 3] mm
• Manual segmentation by a board certified
radiologist
• 100 pairs for training; 40 for validation; 73 for
testing
• Jaccard index and AUC (AUC is a supplemental
metric and evaluated on subset of samples)
Fig 4: Segmentation results of 2 cases (a, b) showing rule-based method
[2] on the left, and proposed CLF w context on the right. TP, FP, FN. Red
arrows indicate regions where false positives were reduced.
• CLFw context improves the Jaccard index by 0.09 over
the CLFw/o context (Table 1), largely due to the
discriminative power of the context features (84%
feature importance, Fig. 3)
• CLFw context provides incremental improvement
(Jaccard index increase of 0.08) over the rule-based
method [2] (Table 1). Fig 4 demonstrates 2 cases
where the # of false positives were reduced
compared to method [2]
Feature type Feature importance
Symmetry (context)
Landmark (context)
Offset (context)
Intensity + Texture
Landmark detection
• ASM search matches HOG descriptors using the
Mahalanobis distance, searching horizontally,
vertically, and diagonally, at 3 resolutions (Fig. 2a)
Features
• Context features: Offset vector to each landmark
(Fig. 2b), intensity difference w.r.t. each landmark,
intensity difference w.r.t. symmetric point across
the midline
• Intensity: 0th, 1st, 2nd order scale-space features
• Texture: Grey level cooccurrence matrix properties
• Note: all features using intensity are computed at
3 Gaussian scales (1, 2, 4 pixels)
Fig. 1: Workflow for proposed tumor segmentation method
Sampling for training
• Negatives sampled from a probability map of
regions prone to false positives (Fig. 2c)
Classifier Training
• Axis-aligned RF using the Gini impurity measure,
with 100 trees and 20 random features at each
split node.
Bone scan
Intensity and
texture features
[2] CLFw context
(a) (b) (c)
Landmark detection
Context features
Classification
(CLFw context)
Tumor segmentation
0 0.25 0.5
Method Jaccard AUC
[2] 0.50 ± 0.31 --
CLF w/o context 0.49 ± 0.26 0.91 ± 0.05
CLF w context 0.58 ± 0.27 0.96 ± 0.03
[2] CLFw context
(a) (b)
Fig 3: Gini feature importance from RF training
Table 1: Jaccard and AUC metrics for rule-based method [2], CLF w/o
context features, and CLF w/ context features