Handling missing MRI sequences in deep learning segmentation of brain metastases: a multicenter study

Abstract

The purpose of this study was to assess the clinical value of a deep learning (DL) model for automatic detection and segmentation of brain metastases, in which a neural network is trained on four distinct MRI sequences using an input-level dropout layer, thus simulating the scenario of missing MRI sequences by training on the full set and all possible subsets of the input data. This retrospective, multicenter study, evaluated 165 patients with brain metastases. The proposed input-level dropout (ILD) model was trained on multisequence MRI from 100 patients and validated/tested on 10/55 patients, in which the test set was missing one of the four MRI sequences used for training. The segmentation results were compared with the performance of a state-of-the-art DeepLab V3 model. The MR sequences in the training set included pre-gadolinium and post-gadolinium (Gd) T1-weighted 3D fast spin echo, post-Gd T1-weighted inversion recovery (IR) prepped fast spoiled gradient echo, and 3D fluid attenuated inversion recovery (FLAIR), whereas the test set did not include the IR prepped image-series. The ground truth segmentations were established by experienced neuroradiologists. The results were evaluated using precision, recall, Intersection over union (IoU)-score and Dice score, and receiver operating characteristics (ROC) curve statistics, while the Wilcoxon rank sum test was used to compare the performance of the two neural networks. The area under the ROC curve (AUC), averaged across all test cases, was 0.989 ± 0.029 for the ILD-model and 0.989 ± 0.023 for the DeepLab V3 model (p = 0.62). The ILD-model showed a significantly higher Dice score (0.795 ± 0.104 vs. 0.774 ± 0.104, p = 0.017), and IoU-score (0.561 ± 0.225 vs. 0.492 ± 0.186, p < 0.001) compared to the DeepLab V3 model, and a significantly lower average false positive rate of 3.6/patient vs. 7.0/patient (p < 0.001) using a 10 mm3 lesion-size limit. The ILD-model, trained on all possible combinations of four MRI sequences, may facilitate accurate detection and segmentation of brain metastases on a multicenter basis, even when the test cohort is missing input MRI sequences.

Prospective Deployment of Deep Learning in MRI: A Framework for Important Considerations, Challenges, and Recommendations for Best Practices

“…8). 100 Recommendations For determining beneficial use cases for MRI classification and segmentation, we recommend developing a robust pipeline to ensure that common MRI artifacts do not cause networks to perceive real-world images as OOD. By subsampling the training data, the amount necessary for the given clinical task may be coarsely extrapolated.…”

Multi‐parametric magnetic resonance imaging for radiation treatment planning

“…69 Detection and segmentation of brain metastases using DL has been explored by several groups. [70][71][72][73] With the accumulation of high-quality data and the advances in DL techniques, promising results have been reported in recent years claiming detection sensitivities of more than 0.9 or false-positive rates of less than one metastases per scan. [74][75][76][77] However, individual stateof -the-art models still struggle to achieve both high sensitivity and low false-positive rates at the same time suggesting potential room for improvement using novel network architectures or ensemble of different models.…”

“…Detection and segmentation of brain metastases using DL has been explored by several groups 70–73 . With the accumulation of high‐quality data and the advances in DL techniques, promising results have been reported in recent years claiming detection sensitivities of more than 0.9 or false‐positive rates of less than one metastases per scan 74–77 .…”

Preoperative Brain Tumor Imaging: Models and Software for Segmentation and Standardized Reporting

“…The segmentation of other brain tumor types has been sparsely investigated in the literature in comparison, possibly due to a lack of open-access annotated data, as illustrated by recent reviews or studies investigating brain tumor segmentation in general ( 22 , 23 ). Grovik et al used a multicentric and multi-sequence dataset of 165 metastatic patients to train a segmentation model with the DeepLabV3 architecture ( 24 , 25 ). The best segmentation results were around 79% Dice score with 3.6 false positive detections per patient on average.…”