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Semi-Automatic Segmentation Software for Quantitative Clinical Brain Glioblastoma Evaluation

Rationale and Objectives

Quantitative measurement provides essential information about disease progression and treatment response in patients with glioblastoma multiforme (GBM). The goal of this article is to present and validate a software pipeline for semi-automatic GBM segmentation, called AFINITI (Assisted Follow-up in NeuroImaging of Therapeutic Intervention), using clinical data from GBM patients.

Materials and Methods

Our software adopts the current state-of-the-art tumor segmentation algorithms and combines them into one clinically usable pipeline. Both the advantages of the traditional voxel-based and the deformable shape-based segmentation are embedded into the software pipeline. The former provides an automatic tumor segmentation scheme based on T1- and T2-weighted magnetic resonance (MR) brain data, and the latter refines the segmentation results with minimal manual input.

Results

Twenty-six clinical MR brain images of GBM patients were processed and compared with manual results. The results can be visualized using the embedded graphic user interface.

Conclusion

Validation results using clinical GBM data showed high correlation between the AFINITI results and manual annotation. Compared to the voxel-wise segmentation, AFINITI yielded more accurate results in segmenting the enhanced GBM from multimodality MR imaging data. The proposed pipeline could be used as additional information to interpret MR brain images in neuroradiology.

Introduction

Despite the best available standard therapies, including surgery, radiation, and chemotherapy, the survival in patients diagnosed with glioblastoma multiforme (GBM) remains dismal at 14 months . Newer therapeutic strategies aiming at targeting specific molecules are being developed and tested in clinical trials . Temozolomide chemoradiation has significantly prolonged survival but produces pseudoprogression that is difficult or impossible to distinguish from recurrence in 30%–50% of patients . In addition, antiangiogenic therapies have been used in combination with conventional chemotherapy in patients with recurrent GBM, demonstrating radiographic response rates of 35%–50% . These agents improve significantly patient quality of life but alter the pattern of recurrence by a potent effect on tumor permeability, suppressing enhancement within a solid tumor with a resulting increase in the frequency of infiltrative recurrence .

These therapy-induced alterations in the natural history and imaging appearance of treated GBM have made imaging follow-up by conventional magnetic resonance imaging (MRI) difficult, which motivates widespread ongoing research to discover additional imaging biomarkers and has led to a revision in response criteria. Although the most commonly used imaging criteria for evaluating treatment response are still based on measurement of enhancing tumor (the Macdonald Criteria) , the increase in infiltrative recurrence and the difficulty in distinguishing recurrence from progression has led to proposal of a new criteria for tumor response that includes abnormality on T2-weighted or fluid-attenuated inversion recovery images as additional markers for progression (the RANO criteria) . The RANO criteria also recommends the use of volumetric measurements of enhancing tumor because reliance on cross product diameters is problematic and highly operator dependent in cases of irregularly shaped tumor, multifocal tumor, or tumor with cystic or necrotic components. Recently, volumetric measures were found comparable or superior to linear diameter measures as indicators of tumor evaluation.

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Methods

Patients and Data

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The AFINITI Software Pipeline

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Brain tumor segmentation pipeline

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Figure 1, Brain tumor segmentation pipeline.

Figure 2, Registration-based skull stripping method. (a) Original image to be processed; (b) the template image; (c) the segmented brain region of the template image; (d) overlapping the brain region onto the original image.

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Automatic segmentation step

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Figure 3, Some examples of tumor segmentation results after FAST. (a,c) Input images. (b,d) FAST segmentation results.

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Figure 4, Illustration of the combined T1 and T2 segmentation. (a) T2 image registered onto T1 image; (b) overlaying the segmentation result from T1 image onto T2 image; (c) thresholding T2 image ( blue shows the region of interest filtered out ); by adjusting the threshold, we can eliminate the enhanced big vessels close to the tumor; (d) after applying T2 thresholding, the majority of false-positive spots were removed; (e) other isolated spots are removed using morphological operations.

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Interactive processing step

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Results

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Figure 5, Representative segmentation results. Images (a,b) are original; (c,d) are the corresponding segmentation results.

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Table 1

Detailed AFINITI Segmentation Results (mL)

AFINITI Results Manual Results Intersection Union Intersection/AFINITI (%) Intersection/Union (Jaccard Index; %) 6.57 7.26 6.16 7.67 93.7 80.2 20.63 27.60 16.47 31.76 79.8 51.9 28.95 41.32 24.38 45.88 84.2 53.1 2.52 4.27 2.49 4.30 98.6 57.8 53.91 81.80 51.81 83.91 96.1 61.8 22.84 28.03 22.72 28.14 99.5 80.7 10.35 19.65 10.26 19.74 99.1 52.0 21.54 20.79 17.00 25.33 78.9 67.1 4.63 13.96 4.30 14.29 92.8 30.1 6.49 21.09 6.45 21.13 99.4 30.2 1.22 2.27 1.18 2.32 96.5 51.1 3.73 6.62 3.71 6.64 99.5 55.9 40.71 58.44 40.67 58.49 99.9 69.5 8.19 17.13 7.71 17.61 94.2 43.8 35.61 45.60 34.22 46.99 96.1 72.8 15.24 19.99 14.79 20.44 97.1 72.4 12.49 14.74 12.08 15.15 96.8 79.8 7.59 20.48 7.54 20.53 99.3 36.7 1.36 2.34 1.31 2.39 96.6 54.9 10.14 16.04 8.10 18.08 79.9 44.8 17.98 43.75 15.90 45.83 88.4 34.7 64.66 90.07 64.16 90.56 99.2 70.8 1.42 3.13 1.40 3.15 98.5 44.5 39.76 51.85 35.93 55.68 90.4 64.5 9.24 25.32 6.86 27.70 74.3 24.8 14.90 44.10 13.91 45.09 93.3 30.8

AFINITI, Assisted Follow-up in NeuroImaging of Therapeutic Intervention.

Figure 6, Correlation of the segmentation results.

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Figure 7, Difference between manual and semi-automatic segmentations: (a) original image; (b) manual segmentation; (c) semi-automatic segmentation; (d) difference between manual segmentation ( background white shape ) and semi-automatic segmentation ( highlighted red shape ).

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Table 2

Detailed Voxel-based Segmentation Results (mL)

FAST Results Manual Results Intersection Union Intersection/FAST (%) Intersection/Union (Jaccard Index; %) 6.60 7.26 6.16 7.69 93.4 80.1 61.03 27.60 17.04 71.58 27.9 23.8 80.03 41.32 27.39 93.96 34.2 29.2 2.52 4.27 2.49 4.30 98.6 57.8 73.75 81.80 54.34 101.21 73.7 53.7 23.16 28.03 23.03 28.16 99.4 81.8 10.91 19.65 10.72 19.83 98.3 54.1 27.81 20.79 17.20 31.39 61.9 54.8 10.19 13.96 5.70 18.45 55.9 30.9 7.01 21.09 6.54 21.57 93.2 30.3 1.26 2.27 1.21 2.32 96.1 52.0 3.73 6.62 3.71 6.64 99.5 55.9 40.93 58.44 40.86 58.51 99.8 69.8 10.75 17.13 8.19 19.70 76.2 41.6 37.40 45.60 35.71 47.29 95.5 75.5 17.59 19.99 15.50 22.08 88.1 70.2 12.55 14.74 12.14 15.15 96.7 80.1 7.59 20.48 7.54 20.53 99.3 36.7 1.38 2.34 1.33 2.39 96.7 55.8 15.32 16.04 9.03 22.34 58.9 40.4 18.34 43.75 16.11 45.99 87.8 35.0 65.10 90.07 64.51 90.65 99.1 71.2 1.48 3.13 1.46 3.15 98.4 46.2 85.36 51.85 40.13 97.08 47.0 41.3 0.01 25.32 0.00 25.33 0.0 0.0 9.48 44.10 9.42 44.15 99.4 21.3

AFINITI, Assisted Follow-up in NeuroImaging of Therapeutic Intervention.

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Figure 8, Bland-Altman plots of the volume measures between Assisted Follow-up in NeuroImaging of Therapeutic Intervention (AFINITI) and manual results, and between FAST and manual results, respectively.

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Discussion

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Conclusion

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