A brief review on vessel extraction and tracking methods

Extracting an accurate skeletal representation of coronary arteries is an important step for subsequent analysis of angiography images such as image registration and 3D reconstruction of the arterial tree. This step is usually performed by enhancing vessel-like objects in the image, in order to differentiate between blood vessels and background, followed by applying the thinning algorithm to obtain the final output. Another approach is direct extraction of centerline points using exploratory tracing algorithm preceded by a seed point detection schema to provide a set of reliable starting points for the tracing algorithm. A large number of methods fall in these two approaches and this paper aims to contrast them through a brief review of their innate characteristics, associated limitations and current challenges and issues

In situ cane toad recognition

Cane toads are invasive, toxic to native predators, compete with native insectivores, and have a devastating impact on Australian ecosystems, prompting the Australian government to list toads as a key threatening process under the Environment Protection and Biodiversity Conservation Act 1999. Mechanical cane toad traps could be made more native-fauna friendly if they could distinguish invasive cane toads from native species. Here we designed and trained a Convolution Neural Network (CNN) starting from the Xception CNN. The XToadGmp toad-recognition CNN we developed was trained end-to-end using heat-map Gaussian targets. After training, XToadGmp required minimum image pre/post-processing and when tested on 720×1280 shaped images, it achieved 97.1% classification accuracy on 1863 toad and 2892 not-toad test images, which were not used in training