The imaging of pulmonary venous anatomy has traditionally been performed with echocardiography and catheter pulmonary angiography. Magnetic resonance imaging (MRI) and notably multi-detector row computed tomography (CT) have refined the usual imaging techniques. Anomalous pulmonary venous drainage (APVD) is the drainage of one or more pulmonary veins outside the left atrium. Its detection is critical due to the strong association with congenital heart disease as well as other cardiac and respiratory anomalies, which have significant implications for patient management. The pervasive application of CT combined with the relatively non-specific clinical presentation of APVD has resulted in the increased incidental detection of these anomalies. Knowledge is hence vital as the imaging specialist is now usually the first person to make such a diagnosis. Furthermore, pulmonary veins are an important site for arrhythmogenic foci and radiofrequency ablation of such sites is used in the treatment of refractory atrial fibrillation. Hence an imaging road map of these veins is crucial before any management can take place. This pictorial review will illustrate embryology, normal and variant pulmonary vein anatomy, and varied patterns of APVD. Finally, we discuss the implications in the treatment of atrial fibrillation.
A Fontan circulation is a series of palliative surgical procedures, which result in the diversion of the systemic venous return into pulmonary arterial circulation without passing through a ventricle. It is one of the available surgical strategies for patients with cardiac defects that preclude a successful bi-ventricular repair, which encompass a range of complex anatomy. This surgical repair has gone through a series of modifications since the concept was introduced in 1971. Echocardiography remains a vital tool in assessing patients with Fontan circulations but its limitations are well recognised. Cross-sectional imaging modalities such as cardiac MRI and CT are essential components in the systematic clinical evaluation of these patients. The purpose of this review is to understand the information that can be obtained with each cross-sectional modality as well as highlight the challenges that each modality faces.
The unrelenting threat of poaching has led to increased development of new technologies to combat it. One such example is the use of long wave thermal infrared cameras mounted on unmanned aerial vehicles (UAVs or drones) to spot poachers at night and report them to park rangers before they are able to harm animals. However, monitoring the live video stream from these conservation UAVs all night is an arduous task. Therefore, we build SPOT (Systematic POacher deTector), a novel application that augments conservation drones with the ability to automatically detect poachers and animals in near real time. SPOT illustrates the feasibility of building upon state-of-the-art AI techniques, such as Faster RCNN, to address the challenges of automatically detecting animals and poachers in infrared images. This paper reports (i) the design and architecture of SPOT, (ii) a series of efforts towards more robust and faster processing to make SPOT usable in the field and provide detections in near real time, and (iii) evaluation of SPOT based on both historical videos and a real-world test run by the end users in the field. The promising results from the test in the field have led to a plan for larger-scale deployment in a national park in Botswana. While SPOT is developed for conservation drones, its design and novel techniques have wider application for automated detection from UAV videos.
Hawkweeds (Pilosella spp.) have become a severe and rapidly invading weed in pasture lands and forest meadows of New Zealand. Detection of hawkweed infestations is essential for eradication and resource management at private and government levels. This study explores the potential of machine learning (ML) algorithms for detecting mouse-ear hawkweed (Pilosella officinarum) foliage and flowers from Unmanned Aerial Vehicle (UAV)-acquired multispectral (MS) images at various spatial resolutions. The performances of different ML algorithms, namely eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbours (KNN), were analysed in their capacity to detect hawkweed foliage and flowers using MS imagery. The imagery was obtained at numerous spatial resolutions from a highly infested study site located in the McKenzie Region of the South Island of New Zealand in January 2021. The spatial resolution of 0.65 cm/pixel (acquired at a flying height of 15 m above ground level) produced the highest overall testing and validation accuracy of 100% using the RF, KNN, and XGB models for detecting hawkweed flowers. In hawkweed foliage detection at the same resolution, the RF and XGB models achieved highest testing accuracy of 97%, while other models (KNN and SVM) achieved an overall model testing accuracy of 96% and 72%, respectively. The XGB model achieved the highest overall validation accuracy of 98%, while the other models (RF, KNN, and SVM) produced validation accuracies of 97%, 97%, and 80%, respectively. This proposed methodology may facilitate non-invasive detection efforts of mouse-ear hawkweed flowers and foliage in other naturalised areas, enabling land managers to optimise the use of UAV remote sensing technologies for better resource allocation.
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