Automated segmentation and labeling of subcutaneous mouse implants at 14.1T

Magnetic resonance imaging (MRI) is a valuable tool for studying subcutaneous implants in rodents, providing non-invasive insight into biomaterial conformability and longitudinal characterization. However, considerable variability in existing image analysis techniques, manual segmentation and labeling, as well as the lack of reference atlases as opposed to brain imaging, all render the manual implant segmentation task tedious and extremely time-consuming. To this end, the development of automated and robust segmentation pipelines is a necessary addition to the tools available in rodent imaging research. In this work, we presented and compared commonly used image processing contrast-based segmentation approaches—namely, Canny edge detection, Otsu’s single and multi-threshold methods, and a combination of the latter with morphological operators—with more recently introduced convolutional neural network (CNN-) based models, such as the U-Net and nnU-Net (“no-new-net”). These fully automated end-to-end state-of-the-art neural architectures have shown great promise in online segmentation challenges. We adapted them to the implant segmentation task in mice MRI, with both 2D and 3D implementations. Our results demonstrated the superiority of the 3D nnU-Net model, which is able to robustly segment the implants with an average Dice accuracy of 0.915, and an acceptable absolute volume prediction error of 5.74%. Additionally, we provide researchers in the field with an automated segmentation pipeline in Python, leveraging these CNN-based implementations, and allowing to drastically reduce the manual labeling time from approximately 90 min to less than 5 min (292.959 s ± 6.49 s, N = 30 predictions). The latter addresses the bottleneck of constrained animal experimental time in pre-clinical rodent research.