Fast Contour Validation Method based on MRI...

RADIATION ONCOLOGY

Fast Contour Validation Method based on MRI Texture Features

for MRI-based Online Adaptive Replanning

Ying Zhang, Ergun Ahunbay, William A. Hall, Diane Schott, X. Allen Li

Radiation Oncology, Medical College of Wisconsin

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Introduction• On-line adaptive replanning (OLAR): Correct for inter-fraction anatomy changes

using daily images

• Auto-contouring methods: machine learning, deformable image registration(DIR)

• Manual review and editing of the daily contours is still necessary

• More efficient and robust contour evaluation and validation methods are needed to

facilitate the clinical application of OLAR

Presenter

Presentation Notes

With the introduction of in-room imagers, such as CT-on-rails, cone beam CT (CBCT) and MRI, right now people can visualize patient anatomy on a daily basis. On-line adaptive replanning (OLAR)1 was thus proposed to adapt the radiation plan for inter-fraction anatomy changes using daily images. In recent years, techniques such as machine-learning-based image auto segmentation and deformable image registration (DIR)-based contour propagation methods 3,4 have been used to automatically generate contours on those daily images. However, manual review and editing of the daily contours is still necessary, which is a tedious and time consuming process, and impractical for Online adaptive replanning. More efficient and robust contour evaluation and validation methods are needed to facilitate the clinical application of OLAR

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Purpose• Despite variability in organ shape and position, certain image texture features

within the organ may be invariable.

• The distinct quantitative texture features can be used to distinguish different

tissues.

• A fast and automated contour validation approach by using image texture

features of MR images was proposed.

Presenter

Presentation Notes

Despite variability in organ shape and position, certain image texture features within the organ may be invariable. The distinct quantitative texture features can be used to distinguish different tissues. In this study we propose a fast and automated contour validation approach that uses image texture features extracted from MRI images.

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Methods and MaterialsAccurate & Inaccurate

Contours

Data preparation

Decision Tree Model

Texture Features Extraction

ThresholdsDetermination

Model Training Stage

Acceptable?

Model FeaturesCalculation

Automatic Contour Correction

Replanning&Treatment

Accurate Contour

Yes

No

Model Application Stage

Daily Image Contours

Presenter

Presentation Notes

A decision tree based contour validation model was first constructed at the model training stage and the determined model thresholds will then be applied for newly auto-generated daily contours at the application stage. If the daily contour passes the model criterion, it will be identifies as accurate and can be used directly for the re-planning steps. Otherwise, it will be flagged as inaccurate and then a texture based automatic contour correction will be performed. The automatic validation process will be looped again if it still fails to meet the criterion, manual editing will be require.

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Methods and MaterialsT1 MR images

• 20 pancreatic cancer patients (2 image sets per patient)

Pancreatic head contours

• Ground truth contours: manually generated• Test contours: generated by deformable image registration

26 Texture features

• 1st order : skewness, kurtosis, mean and STD

• 2ed order: Gray Level Co-occurrence Matrix (GLCM)

Presenter

Presentation Notes

We used T1-weighted MR images from 20 pancreatic cancer patients. Each patient has two image sets. For all images, the manually generated pancreatic head contours from a experienced radiation oncologist were regarded as accurate and ground truth contours, while test contours were generated using deformable image registration using a commercial software tool. A total of 26 texture features were calculated including histogram-type features and second-order GLCM-type features.

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Image Pre-processing

Bias Correction1 Denoising2 Standardization

Deformable Image

Registration

Original Images

1. Tustison, N. J., et.al (2010). "N4ITK: improved N3 bias correction." IEEE transactions on medical imaging 29(6): 1310-1320. 2. Tomasi, C. and R. Manduchi (1998). Bilateral filtering for gray and color images. Computer Vision, 1998. Sixth International Conference on, IEEE.

Presenter

Presentation Notes

For the original T1 images, bias correction was first performed to correct the effect of unwanted gradient of magnetic field. Bilateral filtering was used to decrease noise level presented in the images. Then all the images were standardized through a histogram matching approach. Deformable image registrations were then performed to get the deformed contours. And all the texture features were calculated based on the preprocessed images.

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Data Preparation

(a) Accurate contour of pancreas head, (b) Inner shell (green) and outer shell (blue)(c) distance map with contour (red) and core region (20%, blue)(d) 3D visualization of the core region (red) and contour volume (grey)

Generate “core” region Generate distance map and select the 20% of pixels

farthest from the surface Feature normalization Minimize machine-, scanning-, and patient-specific

effects on texture features

Generate inner and outer shell volumes Erode and expand the contour by 4 mm Find errors larger than clinical tolerance (3 mm)

(1)

(3) (4)(2)

Presenter

Presentation Notes

For all the contours, a core region was defined by creating a distance map and selecting the 20% of voxels farthest from the surface of the volume. An outer and inner shell volume were also defined by eroding and expanding the contours by 4 mm, respectively. All features extracted from the inner and outer shell volumes were normalized to the feature values of the core volume to minimize machine scanning and patient specifc effects on texture features.

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Core Region: Hierarchical clustering tree suggests 6 weakly correlated features:

• Kurtosis, Skewness, Mean, GLCM-Entropy, GLCM-InformationMeasureCorrelation1 and GLCM-Homogeneity

Inner and Outer Shells Volumes:GLCM-Inverse Correlation2(PIC2)

• Constant for inner shells

• Changed significantly for outer shells with a small expansion1 1

2 20

( , , , )( , )( )

N N

ICi j o

p i j dP di j

θθ− −

= =

=×∑∑

)𝑝𝑝(𝑖𝑖, 𝑗𝑗,𝜃𝜃,𝑑𝑑 Frequency of co-occurrences of voxel value i to the neighboring voxel value j. 𝜃𝜃/𝑑𝑑: rotational angle/distance between the two voxels

Decision Tree Model

Presenter

Presentation Notes

The first level of the tree deals with the core region. Because most of the features we used are correlated, so we selected six weakly-correlated features by calculating a hierarchical clustering tree and the feature value range calculated from the core regions of ground truth contours was used as the criterion for the first layer of the decision tree. We require that for a test contour to pass the first layer, all the six features should be within the obtained feature value range. The second and third levels of the tree deal with the inner shell and outer shell volumes. We found that the value of GLCM-Inverse Correlation2 was relative constant for inner shells, and changed significantly for outer shells with a small expansion. So we choose this feature as criterion for the next two levels of the decision tree. For a test contour to be identified as accurate, the feature value for inner shell should be smaller than the inner threshold, and the value from outer shell should be larger than the outer threshold.

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Results Thresholds

• Inner Shells, Trein=4.5

• Outer Shells, Treout=1133

Model performance • Sensitivity: 97.5% (39 out of 40)• Specificity: 92.5% (37 out of 40)

• False Negative Rate: 2.5% (1 out of 40)• False Positive Rate: 7.5% (3 out of 40)

Presenter

Presentation Notes

The thresholds were then determined based on the distribution of ground truth and test contours. After the three layer decision tree model, 39 out of 40 accurate contours were correctly labeled as accurate, while 37 out of 40 auto-generated contours were correctly identified as inaccurate. Only 1 out of 40 accurate contours was mislabeled, which is acceptable for clinical practice. Meanwhile, 3 out of 40 inaccurate cases were mislabeled.

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Results

3D DSC IC2in Thre1 IC2out Thre2

P1Good - 1.16

< 4.5

21494

> 1133Auto 0.88 396 41804

P2Good - 1.54 3626

Auto 0.89 1.55 259

P1

P2

Presenter

Presentation Notes

Here we show representative examples of pancreatic head contours with both accurate and inaccurate contours delineated. The corresponding 3D Dice coefficient and calculated texture feature values for inner shell and outer shell are listed in the table. Both cases passed the first layer of the decision tree regarding the core region. As highlighted with organ color, the first cased failed to meet the threshold for inner shell, as surrounding fat tissues were incorrectly included in the pancreatic head contour. Makes the normalized feature value of the inner shell larger than threshold. While second case passed the inner shell criteria but failed at the third layer for outer shell. In general, the auto generated contour is small than the ground truth contour, makes the normalized feature value for outer shell smaller than the threshold.

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Automatic Contour Correction Texture based Active Contour Algorithm (TACA)

(1) (2)(4) (3)

1.Pre-processed MRI image and contours2.GLCM-Cluster Shade feature map 3. Normalized feature map and the Gradient Vector Flow (GVF) 4. Contour evolvement plots

Dataset • flagged inaccurate contours

Automatic contour correction • Voxel-based texture feature map

GLCM-Cluster Shade

• Gradient Vector Flow (GVF)1

• Iterative contour evolvement

1. Xu, Chenyang, and Jerry L. Prince. "Gradient vector flow: A new external force for snakes." Computer Vision and Pattern Recognition, 1997. Proceedings., 1997 IEEE Computer Society Conference on. IEEE, 1997.

Presenter

Presentation Notes

For identified inaccurate contours, a texture based automatic contour correction process were also proposed. A voxel based texture feature map was first calculated. The GLCM-cluster shade was chosen for the contour correction purpose because this feature can highlight the tissue boundaries, as shown at figure 2 here. A Gradient Vector Flow field was then generated for the normalized feature map. This field points to the object boundary from both side of the boundary. It can provide a force that drives the contour to the highlighted boundary on the feature map through an iterative contour evolvement.

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Automatic Contour Correction Texture based Active Contour Algorithm (TACA)

Presenter

Presentation Notes

The performance of the proposed Texture based Contour correction method was presented by two representative cases. Significant improvement was observed on the corrected contours. Dice coefficient was calculated for each slice before and after correction. The mean 2D-DICE was increased from 0.83 to 0.92 for the first case, while from 0.85 to 0.93 for the second case.

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Conclusion We have proposed a 3-tiered decision tree that utilizes quantitative image texture features to rapidly classify accurate and inaccurate contours with high sensitivity and specificity.

This method can reduce the workload in manual contour correction during online adaptation, facilitating the routine practice of OLAR.

With further development, the method can be used as a systematic Contour Quality Assurance tool for MRI-guided online replanning.

Presenter

Presentation Notes

We have proposed a 3-tiered decision tree that utilizes quantitative imaging texture features to rapidly classify accurate and inaccurate contours with high sensitivity and specificity. This method can reduce the workload in manual contour correction during online adaptation, facilitating the routine practice of OLAR. With further development, the method may be used as a systematic Contour Quality Assurance (QA) tool for MRI-guided online replanning.

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Disclosures

This work was partially supported by MCW Meinerz and Fotsch Foundations and

by Elekta AB, Stockholm, Sweden.

A patent application is pending.

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Thank you!

Any questions?

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Evaluation of auto-generated contours 3D

Dice2D Dice 2D HD/mm

Mean Std Mean StdMean 0.83 0.82 0.11 7.74 4.17Std 0.07 0.06 0.06 3.03 2.33Min 0.63 0.61 0.03 3.81 0.96Max 0.90 0.91 0.30 18.52 11.88

( ) { }{ } { }{ }{ }, ,GT auto auto GTGT auto p p p pHD p p Max max min d max min d→ →=( ) ( )

( )2, GT auto

GT auto

p pGT auto

p p

S SDSC p p

S S∩

=+

Presenter

Presentation Notes

To quantify the accuracy of the auto-generated contours, Dice Similarity Coefficient (DSC) 13 and Hausdorff Distance (HD)14 were calculated by comparing auto-generated contours and the corresponding ground truth contours. The DSC is defined as the spatial overlap between the auto-generated and ground truth contours The HD is defined as the maximum distance between any points from two contour sets: The HDs were calculated in 2D in a slice-by-slice fashion for each patient.

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GLCM-InverseCorrelation2 GLCM-InverseCorrelation11 1

10

( , , , )( , )( )

N N

ICi j o

p i j dP di j

θθ− −

= =

=×∑∑

GLCM-Inverse Correlation2(PIC2)• Constant for inner shells

• Changed significantly for outer shells with a small expansionFeature values for inner

and outer shell volumes (-10 mm to +20 mm).

Presenter

Presentation Notes

By systematically eroding and dilating the ground truth contour, we found that the value of GLCM-Inverse Correlation2 was relative constant for inner shells, and changed significantly for outer shells with a small expansion. Because of this attribute we used the value of GLCM-Inverse Correlation2 as criterion for the next two levels of the decision tree.

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Histogram based mean value is not enough to separate accurate and inaccurate contours!

Fast Contour Validation Method based on MRI...

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