Purpose: To quantitatively evaluate the generalizability of a deep learning segmentation tool to MRI data from scanners of different MRI manufacturers and to improve the cross-manufacturer performance by using a manufacturer-adaptation strategy. Materials and Methods: This retrospective study included 150 cine MRI datasets from three MRI manufacturers, acquired between 2017 and 2018 (n = 50 for manufacturer 1, manufacturer 2, and manufacturer 3). Three convolutional neural networks (CNNs) were trained to segment the left ventricle (LV), using datasets exclusively from images from a single manufacturer. A generative adversarial network (GAN) was trained to adapt the input image before segmentation. The LV segmentation performance, end-diastolic volume (EDV), end-systolic volume (ESV), LV mass, and LV ejection fraction (LVEF) were evaluated before and after manufacturer adaptation. Paired Wilcoxon signed rank tests were performed. Results: The segmentation CNNs exhibited a significant performance drop when applied to datasets from different manufacturers (Dice reduced from 89.7% +/- 2.3 [standard deviation] to 68.7% +/- 10.8, P<.05, from 90.6% +/- 2.1 to 59.5% +/- 13.3, P<.05, from 89.2% +/- 2.3 to 64.1% +/- 12.0, P<.05, for manufacturer 1, 2, and 3, respectively). After manufacturer adaptation, the segmentation performance was significantly improved (from 68.7% +/- 10.8 to 84.3% +/- 6.2, P<.05, from 72.4% +/- 10.2 to 85.7% +/- 6.5, P<.05, for manufacturer 2 and 3, respectively). Quantitative LV function parameters were also significantly improved. For LVEF, the manufacturer adaptation increased the Pearson correlation from 0.005 to 0.89 for manufacturer 2 and from 0.77 to 0.94 for manufacturer 3. Conclusion: A segmentation CNN well trained on datasets from one MRI manufacturer may not generalize well to datasets from other manufacturers. The proposed manufacturer adaptation can largely improve the generalizability of a deep learning segmentation tool without additional annotation. (C) RSNA, 2020