Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network.

ABSTRACT:

Background

We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Methods

We retrospectively collected the ^99mTc-MDP WBS images with confirmed bone lesions from 3352 patients with malignancy. 14,972 bone lesions were delineated manually by physicians and annotated as benign and malignant. The lesion-based differentiating performance of the proposed network was evaluated by fivefold cross validation, and compared with the other three popular CNN architectures for medical imaging. The average sensitivity, specificity, accuracy and the area under receiver operating characteristic curve (AUC) were calculated. To delve t

SUBMITTER: Liu Y 

PROVIDER: S-EPMC8417997 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature