Interactive segmentation algorithms such as GrowCut usually require quite a few user interactions to
perform well, and have poor repeatability. In this study, we developed a novel technique to boost the
performance of the interactive segmentation method GrowCut involving: 1) a novel "focused
sampling" approach for supervised learning, as opposed to conventional random sampling; 2)
boosting GrowCut using the machine learned results. We applied the proposed technique to the
glioblastoma multiforme (GBM) brain tumor segmentation, and evaluated on a dataset of ten cases
from a multiple center pharmaceutical drug trial. The results showed that the proposed system has
the potential to reduce user interaction while maintaining similar segmentation accuracy.
It is a challenging task to automatically segment glioblastoma multiforme (GBM) brain tumors on T1w post-contrast
isotropic MR images. A semi-automated system using fuzzy connectedness has recently been developed for computing
the tumor volume that reduces the cost of manual annotation. In this study, we propose a an ensemble method that
combines multiple segmentation results into a final ensemble one. The method is evaluated on a dataset of 20 cases from
a multi-center pharmaceutical drug trial and compared to the fuzzy connectedness method. Three individual methods
were used in the framework: fuzzy connectedness, GrowCut, and voxel classification. The combination method is a
confidence map averaging (CMA) method. The CMA method shows an improved ROC curve compared to the fuzzy
connectedness method (p < 0.001). The CMA ensemble result is more robust compared to the three individual methods.
Inconsistency and a lack of reproducibility are commonly associated with semi-automated segmentation methods. In this
study, we developed an ensemble approach to improve reproducibility and applied it to glioblastoma multiforme (GBM)
brain tumor segmentation on T1-weigted contrast enhanced MR volumes. The proposed approach combines samplingbased
simulations and ensemble segmentation into a single framework; it generates a set of segmentations by perturbing
user initialization and user-specified internal parameters, then fuses the set of segmentations into a single consensus
result. Three combination algorithms were applied: majority voting, averaging and expectation-maximization (EM). The
reproducibility of the proposed framework was evaluated by a controlled experiment on 16 tumor cases from a multicenter
drug trial. The ensemble framework had significantly better reproducibility than the individual base Otsu
thresholding method (p<.001).
This study applied a Gaussian Mixture Model (GMM) to apparent diffusion coefficient (ADC) histograms to evaluate
glioblastoma multiforme (GBM) tumor treatment response using diffusion weighted (DW) MR images. ADC mapping,
calculated from DW images, has been shown to reveal changes in the tumor's microenvironment preceding
morphologic tumor changes. In this study, we investigated the effectiveness of features that represent changes from
pre- and post-treatment tumor ADC histograms to detect treatment response. The main contribution of this work is to
model the ADC histogram as the composition of two components, fitted by GMM with expectation maximization (EM)
algorithm. For both pre- and post-treatment scans taken 5-7 weeks apart, we obtained the tumor ADC histogram,
calculated the two-component features, as well as the other standard histogram-based features, and applied supervised
learning for classification. We evaluated our approach with data from 85 patients with GBM under chemotherapy, in
which 33 responded and 52 did not respond based on tumor size reduction. We compared AdaBoost and random
forests classification algorithms, using ten-fold cross validation, resulting in a best accuracy of 69.41%.
Multi-Slice Computed Tomography (MSCT) imaging of the lungs allow for detection and follow-up of very small
lesions including solid and ground glass nodules (GGNs). However relatively few computer-based methods have been
implemented for GGN segmentation. GGNs can be divided into pure GGNs and mixed GGNs, which contain both nonsolid
and solid components (SC). This latter category is especially of interest since some studies indicate a higher
likelihood of malignancy in GGNs with SC. Due to their characteristically slow growth rate, GGNs are typically
monitored with multiple follow-up scans, making measurement of the volume of both solid and non-solid component
especially desirable. We have developed an automated method to estimate the SC percentage within a segmented GGN.
First, the SC algorithm uses a novel method to segment out the solid structures, while excluding any vessels passing near
or through the nodule. A gradient distribution analysis around solid structures validates the presence or absence of SC.
We tested 50 GGNs, split between three groups: 15 GGNs with SC, 15 GGNs with a solid nodule added to simulate SC,
and 20 GGNs without SC. With three defined satisfaction levels for the segmentation (A: succeed, B: acceptable, C:
failed), the first group resulted in 60% with score A, 40% with score B, 0% with score C. The second group resulted in
66.7% with score A and 33.3% with score B. In testing the first and 3rd groups, the algorithm correctly detected SC in
all cases where it was present (sensitivity of 100%) and correctly determined absence of SC in 15 out of 20 cases
(specificity 75%).
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