Abstract
Stereotactic radio surgery (SRS) is the preferred treatment for brain metastases (BM), in which the delineation of metastatic lesions is one of the critical steps. Taking into consideration that the BM always have clear boundary with surrounding tissues but very small volume, the difficulty of delineation is object detection instead of segmentation. In this paper, we presented a novel lesion detection framework, called Multi-scale and Multi-level Single Shot Detector (MMSSD), to detect the BM target accurately and effectively. In MMSSD, we took advantage of multi-scale feature maps, while paid more attention on the shallow layers for small objects. Specifically, first we only preserved the applicable large-and-middle-scale features in SSD, then generated new feature representations by multi-level feature fusion module, and finally made predictions on those feature maps. The proposed MMSSD framework was evaluated on the clinical dataset, and the experiment results demonstrated that our method outperformed existing popular detectors for BM detection.
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Acknowledgments
Publications of this article were sponsored by the National Science Foundation of China under Grant 61902264 and by the Key Research and Development projects in Sichuan Province under Grant 2019YFS0125.
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Yu, H., Xia, W., Liu, Y., Gu, X., Zhou, J., Zhang, Y. (2021). MMSSD: Multi-scale and Multi-level Single Shot Detector for Brain Metastases Detection. In: Crimi, A., Bakas, S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science(), vol 12658. Springer, Cham. https://doi.org/10.1007/978-3-030-72084-1_12
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