Fig. 9: Overview over the AutoDVT prototype core algorithm.
From: Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning
a whole overview and b overview over the individual branches. A U-Net41 serves as a backbone for automatic delineation of vein and arteries (b). The prediction of the anatomical location of the image is based on our previous work15. Network branches predict the anatomical location and whether the vessel is open or closed under pressure. Landmark predictions are performed from the learned numeric representation in the bottleneck layer; vessel compression state is predicted from the output segmentation mask. The network components are connected and can be trained through back-propagation42 in an end-to-end manner. The input is a stack of nine images (individual video frame images resampled to 150 × 150 pixels) from an ultrasound video stream that moves by one in a sliding window fashion. A single segmentation mask is produced for the last-most image within approximately 25 ms. Two separate models with identical architecture are trained, one for the groin area (LM0–LM5) and one for the knee area (LM8–LM10). Each model holds 31,475,527 parameters. (OC = open/close).
