Temporal Recurrent Networks for Online Action Detection

Most work on temporal action detection is formulated as an offline problem, in which the start and end times of actions are determined after the entire video is fully observed. However, important real-time applications including surveillance and driver assistance systems require identifying actions as soon as each video frame arrives, based only on current and historical observations. In this paper, we propose a novel framework, Temporal Recurrent Network (TRN), to model greater temporal context of a video frame by simultaneously performing online action detection and anticipation of the immediate future. At each moment in time, our approach makes use of both accumulated historical evidence and predicted future information to better recognize the action that is currently occurring, and integrates both of these into a unified end-to-end architecture. We evaluate our approach on two popular online action detection datasets, HDD and TVSeries, as well as another widely used dataset, THUMOS'14. The results show that TRN significantly outperforms the state-of-the-art

De sluitsteen van de zorg

SAMENVATTING - Vermijdbare fouten in de gezondheidszorg vormen een groot probleem. Van de in ziekenhuizen opgenomen patiënten krijgt 2 procent ermee te maken. - Belangrijke voorwaarden om tot verbeteringen te komen zijn de aanwezigheid van een elektronisch patiëntendossier (dat bij voorkeur patiëntengegevens van huisarts en zorginstellingen combineert), zorg­protocollen en daarnaast en (dat is nieuw) een beslissings­onder­steunend systeem. - Deskundigen verwachten dat toepassing van beslissingsondersteuning een (noodzakelijke) revolutie in het falende gezondheidszorgsysteem zal teweegbrengen. - In dit artikel wordt een dergelijk systeem, Gaston, beschreven, inclusief de eerste ervaringen op verschillende afdelingen van het Catharina Ziekenhuis in Eindhoven. - Er wordt een oproep gedaan aan met name de Orde van Medisch Specialisten, wetenschappelijke verenigingen en het CBO om deze revolutie in Nederland vorm te geve

Cardiac image segmentation for contrast agent videodensitometry

Indicator dilution techniques are widely used in the intensive care unit and operating room for cardiac parameter measurements. However, the invasiveness of current techniques represents a limitation for their clinical use. The development of stable ultrasound contrast agents allows new applications of the indicator dilution method. Ultrasound contrast agent dilutions permit an echographic noninvasive measurement of cardiac output, ejection fraction, and blood volumes. The indicator dilution curves are measured by videodensitometry of specific regions of interest and processed for the cardiac parameter assessment. Therefore, the major indicator dilution imaging issue is the detection of proper contrast videodensitometry regions that maximize the signal-to-noise ratio of the measured indicator dilution curves. This work presents an automatic contour detection algorithm for indicator dilution videodensitometry. The algorithm consists of a radial filter combined with an outlier correction. It maximizes the region of interest by excluding cardiac structures that act as interference to the videodensitometric analysis. It is fast, projection independent, and allows the simultaneous detection of multiple contours in real time. The system is compared to manual contour definition on both echographic and magnetic resonance images

On the physical and stochastic representation of an indicator dilution curve as a gamma variate

The analysis of intravascular indicator dynamics is important for cardiovascular diagnostics as well as for the assessment of tissue perfusion, aimed at the detection of ischemic regions or cancer hypervascularization. To this end, indicator dilution curves are measured after the intravenous injection of an indicator bolus and fitted by parametric models for the estimation of the hemodynamic parameters of interest. Based on heuristic reasoning, the dilution process is often modeled by a gamma variate. In this paper, we provide both a physical and stochastic interpretation of the gamma variate model. The accuracy of the model is compared with the local density random walk model, a known model based on physics principles. Dilution curves were measured by contrast ultrasonography both in vitro and in vivo (20 patients). Blood volume measurements were used to test the accuracy and clinical relevance of the estimated parameters. Both models provided accurate curve fits and volume estimates. In conclusion, the proposed interpretations of the gamma variate model describe physics aspects of the dilution process and lead to a better understanding of the observed parameters, increasing the value and credibility of the model, and possibly expanding its diagnostic applications

Influence of intraoperative hypotension on leaks after sleeve gastrectomy

\u3cp\u3eBACKGROUND: Leak after a sleeve gastrectomy (SG) is a severe complication. Risk factors, such as regional ischemia, increased intraluminal pressure, technical failure of the stapling device, and surgeon error, have been reported.\u3c/p\u3e\u3cp\u3eOBJECTIVES: It was hypothesized that intraoperative hypotension is another risk factor for leak, similar to that reported for colorectal surgery.\u3c/p\u3e\u3cp\u3eSETTING: Tertiary teaching hospital in The Netherlands.\u3c/p\u3e\u3cp\u3eMETHODS: Results of a 7-year cohort of primary SGs were reviewed in relation to multiple intraoperative blood pressure measurements. The thresholds for the mean pressure were 40 to 70 mm Hg and for the systolic pressure 70 to 100 mm Hg. Only continuous episodes of 15 and 20 minutes were included.\u3c/p\u3e\u3cp\u3eRESULTS: Twenty-four leaks were identified in a cohort of 1041 primary SGs. Episodes of systolic blood pressure<100 mm Hg for 15 min (P = .027) and 20 minutes (P = .008) were significantly related to a staple line leak. An episode of mean blood pressure<70 mm Hg for 20 min was significantly related to leak (P = .014). Episodes with lower thresholds of pressure occurred less frequently and revealed no significant differences. Other identified risk factors were smoking (P = .019), fast-track recovery program (P = .006), use of a tri-stapler (P = .004), and duration of surgery (P = .000). In a multivariate analysis, only intraoperative systolic pressure<100 mm Hg for 20 minutes remained significant (odds ratio, 2.45; P = .012).\u3c/p\u3e\u3cp\u3eCONCLUSIONS: Intraoperative hypotension may contribute independently to a leak after SG.\u3c/p\u3

System modeling and identification in indicator dilution method for assessment of ejection fraction and pulmonary blood volume

Clinically relevant cardiovascular parameters, such as pulmonary blood volume (PBV) and ejection fraction (EF), can be assessed through indicator dilution techniques. Among these techniques, which are typically invasive due to the need for central catheterization, contrast ultrasonography provides a new emerging minimally invasive option. PBV and EF are then measured by a dilution system identification algorithm after detection of multiple dilution curves by an ultrasound scanner. In this paper, dilution systems are represented by parametric models. Since the measured indicator dilution curves (IDCs) are corrupted by measurement artifacts and outliers, the use of conventional least square error (LSE) estimator for estimating system parameters is not optimal. Different estimators are therefore proposed for estimating the system parameters. Comparison of these estimators with the LSE estimator in assessing EF and PBV is performed on simulated, in vitro and patient data. The results show that the proposed total least absolute deviation estimator (TLAD) outperforms other estimators. The measured IDCs are highly corrupted by noise, which affect the estimation of EF and PBV. Therefore, a two stage denoising method capable of removing outliers is also proposed for removing noise in IDCs

Localization of prostate cancer by contrast-ultrasound diffusion imaging

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Prostate cancer localization by contrast-ultrasound diffusion imaging

Prostate cancer is the form of cancer with the highest incidence in men. The invasiveness or low specificity of available diagnostics hampers a timely use of efficient focal therapies. New imaging techniques are therefore needed for an early prostate cancer localization. We propose a new ultrasound imaging method for prostate cancer localization based on quantification of the (intravascular) local diffusion of ultrasound contrast agents. Local diffusion is expected to correlate better than perfusion with cancer microvascular growth and, therefore, aggressiveness. Local diffusion is estimated by transrectal ultrasound imaging of an ultrasound contrast-agent bolus passing through the prostate circulation after a peripheral intravenous injection. A time-intensity curve (TIC) is measured at each pixel by acoustic quantification. The measured TICs are fitted by a modified Local Density Random Walk model, a solution of the convective diffusion equation that provides a physical representation of the diffusion process. The obtained parametric diffusion images were compared with the histology results after radical prostatectomy. The resulting receiver operating characteristics (curve area = 0.91) were superior to those obtained by estimation of perfusion related parameters. Although extensive validation is necessary, contrast ultrasound diffusion imaging is a promising method, with potential to assess cancer aggressiveness

Strategy for implementation and First results of advanced clinical decision support in hospital pharmacy practice

Clinical decision support systems (CDSS) are the new generation clinical support tools that ‘make it easy to do it right’. Despite promising results, these systems are not common practice, although experts agree that the necessary revolution in health care will depend on its implementation. To accelerate adoption a strategy is handed for structured development and validation of CDSS' content (clinical rules). The first results show that the proposed strategy is easily applicable for creating specific and reliable rules, generating relevant recommendations