Robust Tissue Segmentation in Synchrotron X-Ray Images Using Knowledge Distillation

The usage of advanced computational methods in clinical diagnostic procedures is a long-studied field. In this area, deep learning models show the highest degree of performance, but they require large amounts of annotated training data to achieve that performance. However, the process of annotating biomedical data requires time and is expensive since it must be done by medical professionals. For example, synchrotron X-ray tomographic imaging has been used for endomyocardial biopsy (EMB) sample scanning, which greatly increases resolution and clarity of acquired samples compared to other tomographic imaging techniques. The problem is that in this case, the amount of annotated data is several orders of magnitude smaller than the amount of raw data. This is especially evident in the area of image segmentation, where class annotations need to be accurately done for each pixel in the image, greatly increasing the annotation effort needed. Recent advances in foundational segmentation models offer us a way to partially automate the segmentation using limited oversight, allowing for the creation of partially supervised segmentation masks, especially in volumetric data such as CT or synchrotron X-ray images. In this work, we present a method for robust tissue segmentation in synchrotron X-ray images of endomyocardial biopsy samples in the case of a sparse set of tissue annotations. We employ a large foundational segmentation model from the Segment Anything 2 family to create a set of annotations on a small subset of tissues, using only limited supervision on a few key frames. We then use those frames to propagate annotations through a tissue volume, allowing us to get a large set of initial noisy annotations. These annotations are then used to train a smaller deep segmentation model. The final results are compared against the small set of annotations done by medical professionals to determine model performance. These results show that this approach greatly improves performance when compared to traditional image processing techniques or to models trained only on a small set of professionally annotated images.