Deep-learning-based Detection and Segmentation-assisted Management on Brain Metastases

Deep-learning-based Detection and Segmentation-assisted Management on Brain Metastases:

Abstract

Background

Three-dimensional T1-magnetization-prepared rapid gradient-echo (3D-T1-MPRAGE) is preferred in detecting brain metastases (BMs) among MRI. We developed an automatic deep-learning-based detection and segmentation method for BMs (named BMDS net) on 3D-T1-MPRAGE images and evaluated its performance.

Methods

The BMDS net is a cascaded 3D fully convolution network (FCN) to automatically detect and segment BMs. In total, 1,652 patients with 3D-T1-MPRAGE images from three hospitals (1,201, 231 and 220, respectively) were retrospectively included. Manual segmentations are obtained by a neuroradiologist and a radiation oncologist in a consensus reading in 3D-T1-MPRAGE images. Sensitivity, specificity and dice ratio of the segmentation were evaluated. Specificity and sensitivity measure the fractions of relevant segmented voxels. Dice ratio was used to quantitatively measure the overlap between automatic and manual segmentation results. Paired samples t tests and analysis of variance were employed for statistical analysis.

Results

The BMDS net can detect all BMs, providing a detection result with an accuracy of 100%. Automatic segmentations correlated strongly with manual segmentations through 4-fold cross validation of the dataset with 1,201 patients: the sensitivity was 0.96±0.03 (range, 0.84-0.99), the specificity was 0.99±0.0002 (range, 0.99-1.00), and the dice ratio was 0.85 ± 0.08 (range, 0.62-0.95) for total tumor volume. Similar performance on the other two datasets also demonstrate the robustness of BMDS net in correctly detecting and segmenting BMs in various settings.

Conclusions

The BMDS net yields the accurate detection and segmentation of BMs automatically and could assist stereotactic radiotherapy management for the diagnosis, therapy planning and follow up.