Automatic Annotation to Train ROI Detection Algorithm For Premature Infant Respiration Monitoring in NICU

Visual monitoring of vital parameters of premature infants has become a heavily researched topic in recent years. Respiration rate (RR) is one of the most essential vital sign of newborns, therefore non-contact measurement of respiration is also a strongly studied area. Most of the published algorithms are able to provide better results if a suitable "region of interest" (ROI) detection takes place before the estimation of RR. This ROI is typically generated with a data-driven segmentation method. However, modern deep learning-based ROI detection algorithms require several thousands of annotated samples for training. Data collection and annotation is a long and tedious process. In this work, we propose a motion periodicity based solution to automatically detect the respiration mask containing the belly or the back of neonates. The places of the automatically generated masks showed a 96\% agreement with the places of the manually marked regions. We showed that by using these automatically generated respiration masks for training U-Net variants we can not just avoid the manual labelling, but also reach greater accuracy in the ultimate RR calculation. We concluded, that it is possible and worthwhile to automatically generate annotated dataset for deep learning based ROI detectors in the mentioned field

99mTc-labelled nanosystem as tumour imaging agent for SPECT and SPECT/CT modalities

We report the synthesis, in vitro and in vivo investigation of folate-targeted, biocompatible, biodegradableself-assembled nanoparticles radiolabelled with 99mTc, as potential new SPECT or SPECT/CT imagingagent.Nanoparticles with hydrodynamic size in the range of 75?200 nm were prepared by self-assembly ofchitosan and folated poly