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Brain metastases are one of the most common neurologic complications of cancers, occurring in about 30% of all patients with cancer. Moreover, about 40% of brain metastases patients have more than three metastases. Stereotactic radiosurgery (SRS) is a well-established treatment for brain metastases, which requires accurate detection and delineation of the brain metastases. However, manually detecting and locating all the brain metastases can be very time-consuming and laborintensive, which is a big efficiency bottleneck in this typical one-day outpatient SRS procedure. Developing a fast automatic detection tool of brain metastases is highly desirable, but is very challenging given the large number of brain metastases that a patient can have and the small size that a brain metastasis can be. In this work, we propose to use a 3D Mask R-CNN method to automatically and quickly detect the brain metastases on magnetic resonance (MR) images for SRS. At the training stage, coarse feature maps were extracted from 3D MR image patches using pretrained ResNet. Then, a region proposal network (RPN) was used to predict the locations and sizes of the rough candidate tumor ROIs from the coarse feature maps. By using a uniformed fully convolution network (FCN), the metastases within ROI was segmented. The segmentation loss, classification loss (metastases or non-metastases), as well as ROI location and size regression loss were used to supervise the proposed networks. For a new query patient, candidate ROIs and predicted probability maps within ROIs were obtained from our trained model. By aggregating ROIs and the tumor probability maps and performing a consolidation via weighted cluster scoring, the final ROIs of the brain metastases was obtained. We have tested our method on 20 patients’ brain contrast T1-weighted MR images, and achieved 86.5%±3.2% sensitivity and 89.7%±4.8% specificity. For each patient, it took our trained model a few seconds to detect the brain metastases on the 3D MR images. The results of our preliminary study have demonstrated its efficacy and clinical feasibility. This auto-detection method could be a useful tool to significantly improve the efficiency of SRS treatment planning and hence ultimately improve the clinical outcome.
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