Purpose: To develop an approach for computer-aided detection (CAD) of small brain metastases in post-Gd T1-weighted magnetic resonance imaging (MRI).
Method: A set of unevenly spaced 3D spherical shell templates was optimized to localize brain metastatic lesions by cross-correlation analysis with MRI. Theoretical and simulation analyses of effects of lesion size and shape heterogeneity were performed to optimize the number and size of the templates and the cross-correlation thresholds. Also, effects of image factors of noise and intensity variation on the performance of the CAD system were investigated. A nodule enhancement strategy to improve sensitivity of the system and a set of criteria based upon the size, shape and brightness of lesions were used to reduce false positives. An optimal set of parameters from the FROC curves was selected from a training dataset, and then the system was evaluated on a testing dataset including 186 lesions from 2753 MRI slices. Reading results from two radiologists are also included.
Results: Overall, a 93.5% sensitivity with 0.024 of intra-cranial false positive rate (IC-FPR) was achieved in the testing dataset. Our investigation indicated that nodule enhancement was very effective in improving both sensitivity and specificity. The size and shape criteria reduced the IC-FPR from 0.075 to 0.021, and the brightness criterion decreases the extra-cranial FPR from 0.477 to 0.083 in the training dataset. Readings from the two radiologists had sensitivities of 60% and 67% in the training dataset and 70% and 80% in the testing dataset for the metastatic lesions <5 mm in diameter.
Conclusion: Our proposed CAD system has high sensitivity and fairly low FPR for detection of the small brain metastatic lesions in MRI compared to the previous work and readings of neuroradiologists. The potential of this method for assisting clinical decision- making warrants further evaluation and improvements.
Copyright © 2012 Elsevier Inc. All rights reserved.