Automated Brain Metastases Detection Framework for T1-Weighted Contrast-Enhanced 3D MRI

TL;DR

This paper introduces an automated framework for detecting small brain metastases in contrast-enhanced 3D MRI scans, combining candidate selection with a specialized 3D CNN, achieving high sensitivity with manageable false positives.

Contribution

The novel framework effectively detects small brain metastases using a single MRI sequence and a two-stage process, improving detection accuracy over existing methods for tiny lesions.

Findings

Achieved 90% sensitivity with ~9 false positives per patient.

Framework performs comparably to state-of-the-art methods for larger lesions.

Extensive data augmentation enhances model invariance to lesion shape and intensity.

Abstract

Brain Metastases (BM) complicate 20-40% of cancer cases. BM lesions can present as punctate (1 mm) foci, requiring high-precision Magnetic Resonance Imaging (MRI) in order to prevent inadequate or delayed BM treatment. However, BM lesion detection remains challenging partly due to their structural similarities to normal structures (e.g., vasculature). We propose a BM-detection framework using a single-sequence gadolinium-enhanced T1-weighted 3D MRI dataset. The framework focuses on detection of smaller (< 15 mm) BM lesions and consists of: (1) candidate-selection stage, using Laplacian of Gaussian approach for highlighting parts of a MRI volume holding higher BM occurrence probabilities, and (2) detection stage that iteratively processes cropped region-of-interest volumes centered by candidates using a custom-built 3D convolutional neural network ("CropNet"). Data is augmented extensively during training via a pipeline consisting of random gamma correction and elastic deformation stages; the framework thereby maintains its invariance for a plausible range of BM shape and intensity representations. This approach is tested using five-fold cross-validation on 217 datasets from 158 patients, with training and testing groups randomized per patient to eliminate learning bias. The BM database included lesions with a mean diameter of ~5.4 mm and a mean volume of ~160 mm3. For 90% BM-detection sensitivity, the framework produced on average 9.12 false-positive BM detections per patient (standard deviation of 3.49); for 85% sensitivity, the average number of false-positives declined to 5.85. Comparative analysis showed that the framework produces comparable BM-detection accuracy with the state-of-art approaches validated for significantly larger lesions.