No abstract

Background and Purpose-Perfusion-weighted imaging can predict infarct growth in acute stroke and potentially be used to select patients with tissue at risk for reperfusion therapies. However, the lack of consensus and evidence on how to best create PWI maps that reflect tissue at risk challenges comparisons of results and acute decision-making in trials. Deconvolution using an arterial input function has been hypothesized to generate maps of a more quantitative nature and with better prognostic value than simpler summary measures such as time-to-peak or the first moment of the concentration time curve. We sought to compare 10 different perfusion parameters by their ability to predict tissue infarction in acute ischemic stroke. Methods-In a retrospective analysis of 97 patients with acute stroke studied within 6 hours from symptom onset, we used receiver operating characteristics in a voxel-based analysis to compare 10 perfusion parameters: time-to-peak, first moment, cerebral blood volume and flow, and 6 variants of time to peak of the residue function and mean transit time maps. Subanalysis assessed the effect of reperfusion on outcome prediction. Results-The most predictive maps were the summary measures first moment and time-to-peak. First moment was significantly more predictive than time to peak of the residue function and local arterial input function-based methods (PϽ0.05), but not significantly better than conventional mean transit time maps. Conclusion-Results indicated that if a single map type was to be used to predict infarction, first moment maps performed at least as well as deconvolved measures. Deconvolution decouples delay from tissue perfusion; we speculate this negatively impacts infarct prediction.

Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of an agriculture field is homogeneous, and obstacles, like people, animals and other obstacles, occur rarely and are of distinct appearance compared to the field. This paper introduces DeepAnomaly, an algorithm combining deep learning and anomaly detection to exploit the homogenous characteristics of a field to perform anomaly detection. We demonstrate DeepAnomaly as a fast state-of-the-art detector for obstacles that are distant, heavily occluded and unknown. DeepAnomaly is compared to state-of-the-art obstacle detectors including “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks” (RCNN). In a human detector test case, we demonstrate that DeepAnomaly detects humans at longer ranges (45–90 m) than RCNN. RCNN has a similar performance at a short range (0–30 m). However, DeepAnomaly has much fewer model parameters and (182 ms/25 ms =) a 7.28-times faster processing time per image. Unlike most CNN-based methods, the high accuracy, the low computation time and the low memory footprint make it suitable for a real-time system running on a embedded GPU (Graphics Processing Unit).