A multistage deep learning-based solution was developed to automate the estimation of flea beetle damage in oilseed rape plants, improving efficiency and accuracy in field assessments.

To automatically extract the valve-related motion parameters, the proposed AI-based system was developed and analysed on a large dataset. We investigated the robustness and feasibility of the system extensively on MV and TV related motion parameters. The system achieved human-level accuracy and can improve the workflow efficiency, automation and standardization of valve-related acquisitions or analyses.

The analysis of mitral valve motion is known to be relevant in the diagnosis of cardiac dysfunction. Dynamic motion parameters can be extracted from Cardiac Magnetic Resonance (CMR) images. We propose two chained Convolutional Neural Networks for automatic tracking of mitral valve-annulus landmarks on time-resolved 2-chamber and 4-chamber CMR images. The first network is trained to detect the region of interest and the second to track the landmarks along the cardiac cycle. We successfully extracted several motion-related parameters with high accuracy as well as analyzed unlabeled datasets, thereby overcoming time-consuming annotation and allowing statistical analysis over large number of datasets

Phase-contrast (PC) MRI is used to evaluate blood hemodynamics; however, it can be time consuming to process PC-MR data. In this work, we developed a fully automated segmentation algorithm for PC MR images using deep learning (DL). Automated segmentation of aorta and main pulmonary artery from PC MRI scans can be successfully achieved using the DL model