IntroductionManagement of patients with brain metastases is often based on manual lesion detection and segmentation by an expert reader. This is a time- and labor-intensive process, and to that end, this work proposes an end-to-end deep learning segmentation network for a varying number of available MRI available sequences.MethodsWe adapt and evaluate a 2.5D and a 3D convolution neural network trained and tested on a retrospective multinational study from two independent centers, in addition, nnU-Net was adapted as a comparative benchmark. Segmentation and detection performance was evaluated by: (1) the dice similarity coefficient, (2) a per-metastases and the average detection sensitivity, and (3) the number of false positives.ResultsThe 2.5D and 3D models achieved similar results, albeit the 2.5D model had better detection rate, whereas the 3D model had fewer false positive predictions, and nnU-Net had fewest false positives, but with the lowest detection rate. On MRI data from center 1, the 2.5D, 3D, and nnU-Net detected 79%, 71%, and 65% of all metastases; had an average per patient sensitivity of 0.88, 0.84, and 0.76; and had on average 6.2, 3.2, and 1.7 false positive predictions per patient, respectively. For center 2, the 2.5D, 3D, and nnU-Net detected 88%, 86%, and 78% of all metastases; had an average per patient sensitivity of 0.92, 0.91, and 0.85; and had on average 1.0, 0.4, and 0.1 false positive predictions per patient, respectively.Discussion/ConclusionOur results show that deep learning can yield highly accurate segmentations of brain metastases with few false positives in multinational data, but the accuracy degrades for metastases with an area smaller than 0.4 cm2.
Deep learning is proven to help with common medical image processing procedures, namely segmentation. Labeling data is a core requirement for training a deep learning model; this is time-consuming and expert annotators are in short supply. Strategies that lower data annotation requirements are highly desirable. In this study, we adapt cross-pseudo supervision (CPS) for 3D medical segmentation, a state-of-art semi-supervised deep-learning method where labeled and unlabeled data are used in conjunction to further improve the resulting model. Using the 2021 BraTS dataset, a fully labeled publicly available brain tumor dataset, we train CPS-based networks using a varying number of labeled and unlabeled samples and compare the resulting models against the fully-supervised baseline. The results show that CPS improves performance scores across all combinations of dataset sizes, with an increase in the Dice similarity coefficient (DSC) of 2.6-4.2% and a decrease in the 95th percentile Hausdorff distance (95% HD) of 24-27%.
Objective To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and methods A cascading deep learning reconstruction framework (reference model) was modified by applying three architectural modifications: input-level dense connections between cascade inputs and outputs, an improved deep learning sub-network, and long-range skip-connections between subsequent deep learning networks. An ablation study was performed, where five model configurations were trained on the NYU fastMRI neuro dataset with an end-to-end scheme conjunct on four- and eightfold acceleration. The trained models were evaluated by comparing their respective structural similarity index measure (SSIM), normalized mean square error (NMSE), and peak signal to noise ratio (PSNR). Results The proposed densely interconnected residual cascading network (DIRCN), utilizing all three suggested modifications achieved a SSIM improvement of 8% and 11%, a NMSE improvement of 14% and 23%, and a PSNR improvement of 2% and 3% for four- and eightfold acceleration, respectively. In an ablation study, the individual architectural modifications all contributed to this improvement for both acceleration factors, by improving the SSIM, NMSE, and PSNR with approximately 2–4%, 4–9%, and 0.5–1%, respectively. Conclusion The proposed architectural modifications allow for simple adjustments on an already existing cascading framework to further improve the resulting reconstructions.
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