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The aim of this paper is to contribute to the ongoing discussion about the design of a post‐ 2015 development framework by proposing indicators to monitor employment outcomes. Our analysis of the current MDG employment indicators shows that measurement prob‐ lems, the inappropriate use of aggregate statistics, ambiguous interpretability, and as‐ sumptions that often do not hold true in the context of developing countries are major shortcomings of the current indicators. Based on this critique, we develop a new set of in‐ dicators for productive employment and decent work. We propose four indicators: (i) the growth of labor value added per worker, (ii) the working poverty rate, (iii) (a) the share of workers receiving less than an absolute labor income and (b) the share of workers receiv‐ ing less than 60 percent of the median labor income. We demonstrate the empirical appli‐ cation of these indicators using the country cases of Uganda and Peru.

The aim of this paper is to contribute to the ongoing discussion about the design of a post-2015 development framework by proposing indicators to monitor employment outcomes. Our analysis of the current MDG employment indicators shows that measurement problems, the inappropriate use of aggregate statistics, ambiguous interpretability, and assumptions that often do not hold true in the context of developing countries are major shortcomings of the current indicators. Based on this critique, we develop a new set of indicators for productive employment and decent work. We propose four indicators: (i) the growth of labor value added per worker, (ii) the working poverty rate, (iii) (a) the share of workers receiving less than an absolute labor income and (b) the share of workers receiving less than 60 percent of the median labor income. We demonstrate the empirical application of these indicators using the country cases of Uganda and Peru.

The aim of this paper is to contribute to the ongoing discussion about the design of a post-2015 development framework by proposing indicators to monitor employment outcomes. Our analysis of the current MDG employment indicators shows that measurement problems, the inappropriate use of aggregate statistics, ambiguous interpretability, and assumptions that often do not hold true in the context of developing countries are major shortcomings of the current indicators. Based on this critique, we develop a new set of indicators for productive employment and decent work. We propose four indicators: (i) the growth of labor value added per worker, (ii) the working poverty rate, (iii) (a) the share of workers receiving less than an absolute labor income and (b) the share of workers receiving less than 60 percent of the median labor income. We demonstrate the empirical application of these indicators using the country cases of Uganda and Peru.

In image-guided cardiac interventions, X-ray imaging and intravascular ultrasound (IVUS) imaging are two often used modalities. Interventional X-ray images, including angiography and fluoroscopy, are used to assess the lumen of the coronary arteries and to monitor devices in real time. IVUS provides rich intravascular information, such as vessel wall composition, plaque, and stent expansions, but lacks spatial orientations. Since the two imaging modalities are complementary to each other, it is highly desirable to co-register the two modalities to provide a comprehensive picture of the coronaries for interventional cardiologists. In this paper, we present a solution for co-registering 2-D angiography and IVUS through image-based device tracking. The presented framework includes learning-based vessel detection and device detections, model-based tracking, and geodesic distance-based registration. The system first interactively detects the coronary branch under investigation in a reference angiography image. During the pullback of the IVUS transducers, the system acquires both ECG-triggered fluoroscopy and IVUS images, and automatically tracks the position of the medical devices in fluoroscopy. The localization of tracked IVUS transducers and guiding catheter tips is used to associate an IVUS imaging plane to a corresponding location on the vessel branch under investigation. The presented image-based solution can be conveniently integrated into existing cardiology workflow. The system is validated with a set of clinical cases, and achieves good accuracy and robustness.

The accurate and robust tracking of catheters and transducers employed during image-guided coronary intervention is critical to improve the clinical workflow and procedure outcome. Image-based device detection and tracking methods are preferred due to the straightforward integration into existing medical equipments. In this paper, we present a novel computational framework for image-based device detection and tracking applied to the co-registration of angiography and intravascular ultrasound (IVUS), two modalities commonly used in interventional cardiology. The proposed system includes learning-based detections, modelbased tracking, and registration using the geodesic distance. The system receives as input the selection of the coronary branch under investigation in a reference angiography image. During the subsequent pullback of the IVUS transducers, the system automatically tracks the position of the medical devices, including the IVUS transducers and guiding catheter tips, under fluoroscopy imaging. The localization of IVUS transducers and guiding catheter tips is used to continuously associate an IVUS imaging plane to the vessel branch under investigation. We validated the system on a set of 65 clinical cases, with high accuracy (mean errors less than 1.5mm) and robustness (98.46% success rate). To our knowledge, this is the first reported system able to automatically establish a robust correspondence between the angiography and IVUS images, thus providing clinicians with a comprehensive view of the coronaries.

This paper presents a new technique of coronary digital subtraction angiography which separates layers of moving background structures from dynamic fluoroscopic sequences of the heart and obtains moving layers of coronary arteries. A Bayeisan framework combines dense motion estimation, uncertainty propagation and statistical fusion to achieve reliable background layer estimation and motion compensation for coronary sequences. Encouraging results have been achieved on clinically acquired coronary sequences, where the proposed method considerably improves the visibility and perceptibility of coronary arteries undergoing breathing and cardiac movements. Perceptibility improvement is significant especially for very thin vessels. Clinical benefit is expected in the context of obese patients and deep angulation, as well as in the reduction of contrast dose in normal size patients.

Fluoroscopic images contain useful information that is difficult to comprehend due to the collapse of the 3D information into 2D space. Extracting the informative layers and analyzing them separately could significantly improve the task of understanding the image content. Traditional Digital Subtraction Angiography (DSA) is not applicable for coronary angiography because of heart beat and breathing motion. In this work, we propose a layer extraction method for separating transparent motion layers in fluoroscopic image sequences, so that coronary tree can be better visualized.. The method is based on the fact that different anatomical structures possess different motion patterns, e.g. , heart is beating fast, while lung is breathing slower. A multiscale implementation is used to further improve the efficiency and accuracy. The proposed approach helps to enhance the visibility of the vessel tree, both visually and quantitatively.