In this work, we have developed a novel knowledge-driven quasi-global method for fast and robust registration of thoracic-abdominal CT and cone beam CT (CBCT) scans. While the use of CBCT in operating rooms has become a common practice, there is an increasing demand on the registration of CBCT with pre-operative scans, in many cases, CT scans. One of the major challenges of thoracic-abdominal CT/CBCT registration is from various fields of view (FOVs) of the two imaging modalities. The proposed approach utilizes a priori knowledge of anatomy to generate 2D anatomy targeted projection (ATP) images that surrogate the original volumes. The use of lower dimension surrogate images can significantly reduce the computation cost of similarity evaluation during optimization and make it practically feasible to perform global optimization based registration for image-guided interventional procedures. Another a priori knowledge about the local optima distribution on energy curves is further used to effectively select multi-starting points for registration optimization. 20 clinical data sets were used to validate the method and the target registration error (TRE) and maximum registration error (MRE) were calculated to compare the performance of the knowledge-driven quasi-global registration against a typical local-search based registration. The local search based registration failed on 60% cases, with an average TRE of 22.9mm and MRE of 28.1mm; the knowledge-driven quasi-global registration achieved satisfactory results for all the 20 data sets, with an average TRE of 3.5mm, and MRE of 2.6mm. The average computation time for the knowledge-driven quasi-global registration is 8.7 seconds.
Systematic validation of tumor segmentation technique is very important in ensuring the accuracy and reproducibility
of tumor segmentation algorithm in clinical applications. In this paper, we present a new method for
evaluating 3D tumor segmentation using Artificial Neural Network (ANN) and combined objective metrics. In
our evaluation method, a three-layer feed-forwarding backpropagation ANN is first trained to simulate radiologist's
subjective rating using a set of objective metrics. The trained neural network is then used to evaluate the
tumor segmentation on a five-point scale in a way similar to expert's evaluation. The accuracy of segmentation
evaluation is quantified using average correct rank and frequency of the reference rating in the top ranks of
simulated score list. Experimental results from 93 lesions showed that our evaluation method performs better
than individual metrics. The optimal combination of metrics from normalized volume difference, volume overlap,
Root Mean Square symmetric surface distance and maximum symmetric surface distance showed the smallest
average correct rank (1.43) and highest frequency of the reference rating in the top two places of simulated rating
list (93.55%). Our results also demonstrate that the ANN based non-linear combination method showed better
evaluation accuracy than linear combination method in all performance measures. Our evaluation technique
has the potential to facilitate large scale segmentation validation study by predicting radiologists rating, and to
assist development of new tumor segmentation algorithms. It can also be extended to validation of segmentation
algorithms for other applications.
Graph based semi-automatic tumor segmentation techniques have demonstrated great potential in efficiently measuring
tumor size from CT images. Comprehensive and quantitative validation is essential to ensure the efficacy of graph based
tumor segmentation techniques in clinical applications. In this paper, we present a quantitative validation study of six
graph based 3D semi-automatic tumor segmentation techniques using multiple sets of expert segmentation. The six
segmentation techniques are Random Walk (RW), Watershed based Random Walk (WRW), LazySnapping (LS),
GraphCut (GHC), GrabCut (GBC), and GrowCut (GWC) algorithms. The validation was conducted using clinical CT
data of 29 liver tumors and four sets of expert segmentation. The performance of the six algorithms was evaluated using
accuracy and reproducibility. The accuracy was quantified using Normalized Probabilistic Rand Index (NPRI), which
takes into account of the variation of multiple expert segmentations. The reproducibility was evaluated by the change of
the NPRI from 10 different sets of user initializations. Our results from the accuracy test demonstrated that RW (0.63)
showed the highest NPRI value, compared to WRW (0.61), GWC (0.60), GHC (0.58), LS (0.57), GBC (0.27). The
results from the reproducibility test indicated that GBC is more sensitive to user initialization than the other five
algorithms. Compared to previous tumor segmentation validation studies using one set of reference segmentation, our
evaluation methods use multiple sets of expert segmentation to address the inter or intra rater variability issue in ground
truth annotation, and provide quantitative assessment for comparing different segmentation algorithms.
Comprehensive quantitative evaluation of tumor segmentation technique on large scale clinical data sets is crucial
for routine clinical use of CT based tumor volumetry for cancer diagnosis and treatment response evaluation.
In this paper, we present a systematic validation study of a semi-automatic image segmentation technique for
measuring tumor volume from CT images. The segmentation algorithm was tested using clinical data of 200
tumors in 107 patients with liver, lung, lymphoma and other types of cancer. The performance was evaluated
using both accuracy and reproducibility. The accuracy was assessed using 7 commonly used metrics that can
provide complementary information regarding the quality of the segmentation results. The reproducibility was
measured by the variation of the volume measurements from 10 independent segmentations. The effect of
disease type, lesion size and slice thickness of image data on the accuracy measures were also analyzed. Our
results demonstrate that the tumor segmentation algorithm showed good correlation with ground truth for all
four lesion types (r = 0.97, 0.99, 0.97, 0.98, p < 0.0001 for liver, lung, lymphoma and other respectively). The
segmentation algorithm can produce relatively reproducible volume measurements on all lesion types (coefficient
of variation in the range of 10-20%). Our results show that the algorithm is insensitive to lesion size (coefficient
of determination close to 0) and slice thickness of image data(p > 0.90). The validation framework used in this
study has the potential to facilitate the development of new tumor segmentation algorithms and assist large scale
evaluation of segmentation techniques for other clinical applications.
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