Deep Siamese Network for False Positive Reduction in Brain Metastases Segmentation
Z Yang*, M Chen, R Timmerman, T Dan, Z Wardak, W Lu, X Gu, UT Southwestern Medical Center, Dallas, TX
Presentations
TH-D-TRACK 3-3 (Thursday, 7/29/2021) 2:00 PM - 3:00 PM [Eastern Time (GMT-4)]
Purpose: Stereotactic radiosurgery requires precise target delineation, which can be highly time-consuming especially in patients with multiple (>4) brain metastases (BMs). We have developed a CNN-based BMs segmentation platform (BM-Seg) for accurate and efficient BMs delineation, but it suffers from false-positives caused by varied levels of contrast in planning MRI acquisition. Built on BM-Seg, we developed false-positive reduction Siamese network (FPrSN) to improve BM-Seg performance.
Methods: The proposed FPrSN consists of a parallel feature extractor and a following classifier. The feature extractor utilized a pair of truncated pre-trained ResNet50 with shared weights. In the training stage, paired BM candidate images are used as the input into the feature extractor to 1) increase the training set size by a factor of N/2 and 2) identify the inter-class difference. Then the classifier will randomly take the output from one branch of the feature extractors to classify the corresponding input as a false-positive segmentation or true BM. In the testing stage, only one branch of the feature extractor will be utilized with the classifier to classify a single candidate input as false-positive segmentation or true-positive segmentation. The performance of FPrSN and FPrSN-integrated BM-Seg are evaluated.
Results: The performance of the FPrSN reaches accuracy of 0.94, area under the curve of 0.92, sensitivity of 0.88 and specificity of 0.97 in a testing dataset. The BM-Seg has initial sensitivity of 0.96 and specificity of 0.59 in BMs detection/segmentation, and get improved to 0.86 and 0.89 after integrating FPrSN, respectively. Due to the flexibility of FPrSN, sensitivity/specificity balance of the FPrSN-integrated BM-Seg can be further adjusted to satisfy clinic needs.
Conclusion: This developed FPrSN can effectively reduce the false-positive rate of the BMs segmentation candidates. Integrating FPrSN in the BM-Seg can improve the performance, and this integrated platform could be a beneficial tool for clinical implementation.
Funding Support, Disclosures, and Conflict of Interest: This work is supported by NIH R01 CA235723 and the seed grand from the Department of Radiation Oncology of the UT Southwestern Medical Center.
Handouts
Keywords
Segmentation, Stereotactic Radiosurgery, CAD
Taxonomy
IM/TH- Image Segmentation Techniques: Clustering, classification and threshold
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