Deep-learning 2.5-dimensional single-shot detector improves the performance of automated detection of brain metastases on contrast-enhanced CT

Purpose This study aims to develop a 2.5-dimensional (2.5D) deep-learning, object detection model for the automated detection of brain metastases, into which three consecutive slices were fed as the input for the prediction in the central slice, and to compare its performance with that of an ordinary 2-dimensional (2D) model. Methods We analyzed 696 brain metastases on 127 contrast-enhanced computed tomography (CT) scans from 127 patients with brain metastases. The scans were randomly divided into training ( n = 79), validation ( n = 18), and test ( n = 30) datasets. Single-shot detector (SSD) models with a feature fusion module were constructed, trained, and compared using the lesion-based sensitivity, positive predictive value (PPV), and the number of false positives per patient at a confidence threshold of 50%. Results The 2.5D SSD model had a significantly higher PPV ( t test, p