Detecting multiple lesions of lung cancer-caused metastasis with bone scans using a self-defined object detection model based on SSD framework

Qiang Lin, Xiaoyan Chen, Liangxia Liu, Yongchun Cao, Zhengxing Man, Xianwu Zeng, Xiaodi Huang

Research output: Contribution to journalArticlepeer-review


Objective: To facilitate manual diagnosis of lung cancer-caused metastasis, in this work, we propose a deep learning-based method to automatically identify and locate the hotspots in a bone scan image which denote the lesions metastasized from lung cancer. Approach: An end-to-end metastasis lesion detection model is proposed by following the classical object detection framework SSD (Single Shot multibox object Detector).The proposed model casts lesion detection problem into automatically learning the hierarchal representations of lesion features, locating the spatial position of lesion areas, and boxing the detected lesions. Main results: Experimental evaluation conducted on clinical data of retrospective bone scans shows the comparable performance with a mean score of 0.7911 for AP (Average Precision). A comparative analysis between our network and others including SSD shows the feasibility of the proposed detection network on automatically detecting multiple lesions of metastasis lesions caused by lung cancer. Significance: The proposed method has the potential to be used as an auxiliary tool for improving the accuracy and efficiency of metastasis diagnosis routinely conducted by nuclear medicine physicians.
Original languageUndefined/Unknown
JournalPhysics in Medicine & Biology
Publication statusPublished - 22 Sep 2022

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