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. 2019 Jul;46(7):3133-3141.
doi: 10.1002/mp.13560. Epub 2019 May 21.

Learning-based Automatic Segmentation of Arteriovenous Malformations on Contrast CT Images in Brain Stereotactic Radiosurgery

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Learning-based Automatic Segmentation of Arteriovenous Malformations on Contrast CT Images in Brain Stereotactic Radiosurgery

Tonghe Wang et al. Med Phys. .
Free PMC article


Purpose: Stereotactic radiosurgery (SRS) is widely used to obliterate arteriovenous malformations (AVMs). Its performance relies on the accuracy of delineating the target AVM. Manual segmentation during a framed SRS procedure is time consuming and subject to inter- and intraobserver variation. To address these drawbacks, we proposed a deep learning-based method to automatically segment AVMs on CT simulation image sets.

Methods: We developed a deep learning-based method using a deeply supervised three-dimensional (3D) V-Net with a compound loss function. A 3D supervision mechanism was integrated into a residual network, V-Net, to deal with the optimization difficulties when training deep networks with limited training data. The proposed compound loss function including logistic and Dice losses encouraged similarity and penalized discrepancy simultaneously between prediction and training dataset; this was utilized to supervise the 3D V-Net at different stages. To evaluate the accuracy of segmentation, we retrospectively investigated 80 AVM patients who had CT simulation and digital subtraction angiography (DSA) acquired prior to treatment. The AVM target volume was segmented by our proposed method. They were compared with clinical contours approved by physicians with regard to Dice overlapping, difference in volume and centroid, and dose coverage changes on original plan.

Results: Contours created by the proposed method demonstrated very good visual agreement to the ground truth contours. The mean Dice similarity coefficient (DSC), sensitivity and specificity of the contours delineated by our method were >0.85 among all patients. The mean centroid distance between our results and ground truth was 0.675 ± 0.401 mm, and was not significantly different in any of the three orthogonal directions. The correlation coefficient between ground truth and AVM volume resulting from the proposed method was 0.992 with statistical significance. The mean volume difference among all patients was 0.076 ± 0.728 cc; there was no statistically significant difference. The average differences in dose metrics were all less than 0.2 Gy, with standard deviation less than 1 Gy. No statistically significant differences were observed in any of the dose metrics.

Conclusion: We developed a novel, deeply supervised, deep learning-based approach to automatically segment the AVM volume on CT images. We demonstrated its clinical feasibility by validating the shape and positional accuracy, and dose coverage of the automatic volume. These results demonstrate the potential of a learning-based segmentation method for delineating AVMs in the clinical setting.

Keywords: AVM; CT; deep learning; segmentation.

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