Adversarial robustness improvement for X-ray bone segmentation using synthetic data created from computed tomography scans.

Deep learning-based image analysis offers great potential in clinical practice. However, it faces mainly two challenges: scarcity of large-scale annotated clinical data for training and susceptibility to adversarial data in inference. As an example, an artificial intelligence (AI) system could check patient positioning, by segmenting and evaluating relative positions of anatomical structures in medical images.

Deep learning in computed tomography super resolution using multi-modality data training.

Background: One of the limitations in leveraging the potential of artificial intelligence in X-ray imaging is the limited availability of annotated training data. As X-ray and CT shares similar imaging physics, one could achieve cross-domain data sharing, so to generate labeled synthetic X-ray images from annotated CT volumes as digitally reconstructed radiographs (DRRs). To account for the lower resolution of CT and the CT-generated DRRs as compared to the real X-ray images, we propose the use of super-resolution (SR) techniques to enhance the CT resolution before DRR generation.

USB1 is a miRNA deadenylase that regulates hematopoietic development.

Mutations in the 3' to 5' RNA exonuclease USB1 cause hematopoietic failure in poikiloderma with neutropenia (PN). Although USB1 is known to regulate U6 small nuclear RNA maturation, the molecular mechanism underlying PN remains undetermined, as pre-mRNA splicing is unaffected in patients. We generated human embryonic stem cells harboring the PN-associated mutation c.