TY - GEN
T1 - MMSSD
T2 - 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020
AU - Yu, Hui
AU - Xia, Wenjun
AU - Liu, Yan
AU - Gu, Xuejun
AU - Zhou, Jiliu
AU - Zhang, Yi
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Brain metastases detection
KW - Single shot detector
KW - Small objects
UR - http://www.scopus.com/inward/record.url?scp=85107332722&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85107332722&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-72084-1_12
DO - 10.1007/978-3-030-72084-1_12
M3 - Conference contribution
AN - SCOPUS:85107332722
SN - 9783030720834
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 122
EP - 132
BT - Brainlesion
A2 - Crimi, Alessandro
A2 - Bakas, Spyridon
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 4 October 2020 through 4 October 2020
ER -