Localization of contrast-enhanced breast lesions in ultrafast screening MRI using deep convolutional neural networks.

ABSTRACT:

Objectives

To develop a deep learning-based method for contrast-enhanced breast lesion detection in ultrafast screening MRI.

Materials and methods

A total of 837 breast MRI exams of 488 consecutive patients were included. Lesion's location was independently annotated in the maximum intensity projection (MIP) image of the last time-resolved angiography with stochastic trajectories (TWIST) sequence for each individual breast, resulting in 265 lesions (190 benign, 75 malignant) in 163 breasts (133 women). YOLOv5 models were fine-tuned using training sets containing the same number of MIP images with and without lesions. A long short-term memory (LSTM) network was employed to help reduce false positive predictions. The integrated system was then evaluated on test sets containing

SUBMITTER: Jing X 

PROVIDER: S-EPMC10873226 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature