Improving medical image perception by hierarchical clustering based segmentation

It has been well documented that radiologists' performance is not perfect: they make both false positive and false negative decisions. For example, approximately thirty percent of early lung cancer is missed on chest radiographs when the evidence is clearly visible in retrospect [1]. Currently Computer-Aided Detection (CAD) uses software, designed to reduce errors by drawing radiologists' attention to possible abnormalities by placing prompts on images. Alberdi et al examined the effects of CAD prompts on performance, comparing the negative effect of no prompt on a cancer case with prompts on a normal case. They showed that no prompt on a cancer case can have a detrimental effect on reader sensitivity and that the reader performs worse than if the reader was not using CAD. This became particularly apparent when difficult cases were being read. They suggested that the readers were using CAD as a decision making tool instead of a prompting aid. They conclude that "incorrect CAD can have a detrimental effect on human decisions" [2]. The goal of this paper is to explore the possibility of using Hierarchical Clustering based Segmentation (HCS) [3], as a perceptual aid, to improve the performance of the reader

Tracking human face features in thermal images for respiration monitoring

A method has been developed to track a region related to respiration process in thermal images. The respiration region of interest (ROI) consisted of the skin area around the tip of the nose. The method was then used as part of a non-contact respiration rate monitoring that determined the skin temperature changes caused by respiration. The ROI was located by the first determining the relevant salient features of the human face physiology. These features were the warmest and coldest facial points. The tracking method was tested on thermal video images containing no head movements, small random and regular head movements. The method proved valuable for tracking the ROI in all these head movement types. It was also possible to use this tracking method to monitor respiration rate involving a number of head movement types. Currently, more investigations are underway to improve the tracking method so that it can track the ROI in cases larger head movements

A Novel, Contactless, Portable “Spot-Check” Device Accurately Measures Respiratory Rate

Respiratory rate (RR) is an important vital sign used in the assessment of acutely ill patients. It is also used as to predict serious deterioration in a patient's clinical condition. Convenient electronic devices exist for measurement of pulse, blood pressure, oxygen saturation and temperature. Although devices which measure RR exist, none has entered everyday clinical practice. We developed a contactless portable respiratory rate monitor (CPRM) and evaluated the agreement in respiratory rate measurements between existing methods and our new device. The CPRM uses thermal anemometry to measure breath signals during inspiration and expiration. RR data were collected from 52 healthy adult volunteers using respiratory inductance plethysmography (RIP) bands (established contact method), visual counting of chest movements (established non-contact method) and the CPRM (new method), simultaneously. Two differently shaped funnel attachments were evaluated for each volunteer. Data showed good agreement between measurements from the CPRM and the gold standard RIP, with intra-class correlation coefficient (ICC): 0.836, mean difference 0.46 and 95% limits of agreement of -5.90 to 6.83. When separate air inlet funnels of the CPRM were analysed, stronger agreement was seen with an elliptical air inlet; ICC 0.908, mean difference 0.37 with 95% limits of agreement -4.35 to 5.08. A contactless device for accurately and quickly measuring respiratory rate will be an important triage tool in the clinical assessment of patients. More testing is needed to explore the reasons for outlying measurements and to evaluate in the clinical setting

Tracking 21st century anthropogenic and natural carbon fluxes through model-data integration

Monitoring the implementation of emission commitments under the Paris agreement relies on accurate estimates of terrestrial carbon fluxes. Here, we assimilate a 21st century observation-based time series of woody vegetation carbon densities into a bookkeeping model (BKM). This approach allows us to disentangle the observation-based carbon fluxes by terrestrial woody vegetation into anthropogenic and environmental contributions. Estimated emissions (from land-use and land cover changes) between 2000 and 2019 amount to 1.4 PgC yr−1, reducing the difference to other carbon cycle model estimates by up to 88% compared to previous estimates with the BKM (without the data assimilation). Our estimates suggest that the global woody vegetation carbon sink due to environmental processes (1.5 PgC yr−1) is weaker and more susceptible to interannual variations and extreme events than estimated by state-of-the-art process-based carbon cycle models. These findings highlight the need to advance model-data integration to improve estimates of the terrestrial carbon cycle under the Global Stocktake

Very High Resolution Tree Cover Mapping for Continental United States using Deep Convolutional Neural Networks

Uncertainties in input land cover estimates contribute to a significant bias in modeled above ground biomass (AGB) and carbon estimates from satellite-derived data. The resolution of most currently used passive remote sensing products is not sufficient to capture tree canopy cover of less than ca. 10-20 percent, limiting their utility to estimate canopy cover and AGB for trees outside of forest land. In our study, we created a first of its kind Continental United States (CONUS) tree cover map at a spatial resolution of 1-m for the 2010-2012 epoch using the USDA NAIP imagery to address the present uncertainties in AGB estimates. The process involves different tasks including data acquisition ingestion to pre-processing and running a state-of-art encoder-decoder based deep convolutional neural network (CNN) algorithm for automatically generating a tree non-tree map for almost a quarter million scenes. The entire processing chain including generation of the largest open source existing aerial satellite image training database was performed at the NEX supercomputing and storage facility. We believe the resulting forest cover product will substantially contribute to filling the gaps in ongoing carbon and ecological monitoring research and help quantifying the errors and uncertainties in derived products

Genomic Prediction using Single or Multi-Breed Reference Populations in US Maine-Anjou Beef Cattle

The objective of this study was to estimate accuracies of genomic predictions based on 50K SNP genotypes for 8 nationally evaluated traits in US Maine-Anjou beef cattle using single or multi-breed reference populations. The accuracies of direct genomic values (DGV) ranged from 0.22 to 0.45 for 8 studied traits when the reference populations comprised only 573 Maine-Anjou animals. Accuracies were slightly reduced and ranged from 0.21 to 0.38 when the reference population included over 9,000 animals from many other breeds as well as Maine-Anjou. These results demonstrate that including data from other populations does not generally increase accuracy of prediction in one particular population. This means every breed association must develop its own reference population if it intends to offer genomic prediction

Thermal image processing for real-time noncontact respiration rate monitoring

A real-time thermal imaging based, non-contact respiration rate monitoring method was developed. It measured the respiration related skin surface temperature changes under the tip of the nose. Facial tracking was required as head movements caused the face to appear in different locations in the recorded images over time. The algorithm detected the tip of the nose and then, a region just under it was selected. The pixel values in this region in successive images were processed to determine respiration rate. The segmentation method, used as part of the facial tracking, was evaluated on 55,000 thermal images recorded from 14 subjects with different extent of head movements. It separated the face from image background in all images. However, in 11.7% of the images, a section of the neck was also included, but this did not cause an error in determining respiration rate. The method was further evaluated on 15 adults, against two contact respiration rate monitoring methods that tracked thoracic and abdominal movements. The three methods gave close respiration rates in 12 subjects but in 3 subjects, where there were very large head movements, the respiration rates did not match

climate change and biodiversity in Amazonia: a Late-Holocene perspective

Fire is an important and arguably unnatural component of many wet Amazonian and Andean forest systems. Soil charcoal has been used to infer widespread human use of landscapes prior to European Conquest. An analysis of Amazonian soil carbon records reveals that the records have distinct spatial and temporal patterns, suggesting that either fires were only set in moderately seasonal areas of Amazonia or that strongly seasonal and aseasonal areas are undersampled. Synthesizing data from 300 charcoal records, an age-frequency diagram reveals peaks of fire apparently coinciding with some periods of very strong El Niñ o activity. However, the El Niñ o record does not always provide an accurate prediction of fire timing, and a better match is found in the record of insolation minima. After the time of European contact, fires became much scarcer within Amazonia. In both the Amazonia and the Andes, modern fire pattern is strongly allied to human activity. On the flank of the Andes, forests that have never burned are being eroded by fire spreading downslope from grasslands. Species of these same forests are being forced to migrate upslope due to warming and will encounter a firm artificial fire boundary of human activity

Size and frequency of natural forest disturbances and Amazon carbon balance

Forest inventory studies in the Amazon indicate a large terrestrial carbon sink. However, field plots may fail to represent forest mortality processes at landscape-scales of tropical forests. Here we characterize the frequency distribution of disturbance events in natural forests from 0.01 ha to 2,651 ha size throughout Amazonia using a novel combination of forest inventory, airborne lidar and satellite remote sensing data. We find that small-scale mortality events are responsible for aboveground biomass losses of B1.28 Pg C y 1 over the entire Amazon region. We also find that intermediate-scale disturbances account for losses of B0.01 Pg C y 1 , and that the largest-scale disturbances as a result of blow-downs only account for losses of B0.003 Pg C y 1 . Simulation of growth and mortality indicates that even when all carbon losses from intermediate and large-scale disturbances are considered, these are outweighed by the net biomass accumulation by tree growth, supporting the inference of an Amazon carbon sink