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Currently, increasingly large medical imaging data sets become available for research and are analysed by a range of algorithms segmenting anatomical structures automatically and interactively. While they provide segmentations on a much larger scale than possible to achieve with expert annotators, they are typically less accurate than experts. We present and compare approaches to estimate segmentations on large imaging data sets based on a small number of expert annotated examples, and algorithmic segmentations on a much larger data set. Results demonstrate that combining algorithmic segmentations is reliably outperforming the average individual algorithm. Furthermore, injecting organ specific reliability assessments of algorithms based on expert annotations improves accuracy compared to standard label fusion algorithms. The proposed methods are particularly relevant in putting the results of large image analysis algorithm benchmarks to long-term use.

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Pulmonary embolism is an avoidable cause of death if treated immediately but delays in diagnosis and treatment lead to an increased risk. Computer-assisted image analysis of both unenhanced and contrast-enhanced computed tomography (CT) have proven useful for diagnosis of pulmonary embolism. Dual energy CT provides additional information over the standard single energy scan by generating fourdimensional (4D) data, in our case with 11 energy levels in 3D. In this paper a 4D texture analysis method capable of detecting pulmonary embolism in dual energy CT is presented. The method uses wavelet-based visual words together with an automatic geodesic-based region of interest detection algorithm to characterize the texture properties of each lung lobe. Results show an increase in performance with respect to the single energy CT analysis, as well as an accuracy gain compared to preliminary work on a small dataset.

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Lung image analysis is an essential part in the assessment of pulmonary diseases. Through visual inspection of CT scans, radiologists detect abnormal patterns in the lung parenchyma, aiming to establish a timely diagnosis and thus improving patient outcome. However, in a generalized disorder of the lungs, such as pulmonary hypertension, the changes in organ tissue can be elusive, requiring additional invasive studies to confirm the diagnosis. We present a graph model that quantifies lung texture in a holistic approach enhancing the analysis between pathologies with similar local changes. The approach extracts local state-of-the-art 3D texture descriptors from an automatically generated geometric parcellation of the lungs. The global texture distribution is encoded in a weighted graph that characterizes the correlations among neighboring organ regions. A data set of 125 patients with suspicion of having a pulmonary vascular pathology was used to evaluate our method. Three classes containing 47 pulmonary hypertension, 31 pulmonary embolism and 47 control cases were classified in a one vs. one setup. An area under the curve of up to 0.85 was obtained adding directionality to the edges of the graph architecture. The approach was able to identify diseased patients, and to distinguish pathologies with abnormal local and global blood perfusion defects.

Background: Despite their unique contributions to heart failure (HF) care, home healthcare workers (HHWs) have unmet educational needs and many lack HF caregiving self-efficacy. To address this, we used a community-partnered approach to develop and pilot a HF training course for HHWs. Methods: We partnered with the Training and Employment Fund, a benefit fund of the largest healthcare union in the United States, to develop a 2-hour virtual HF training course that met HHWs’ job-specific needs. English and Spanish-speaking HHWs interested in HF training, with access to Zoom, were eligible. We used a mixed methods design with pre/postsurveys and semi-structured interviews to evaluate the course: (a) feasibility, (b) acceptability, and (c) effectiveness (change in knowledge [Dutch Heart Failure Knowledge Scale range 0−15] and caregiving self-efficacy [HF Caregiver Self-efficacy Scale range 0−100]). Results: Of the 210 HHWs approached, 100 were eligible and agreed, and 70 enrolled. Of them, 53 (employed by 15 different home care agencies) participated. Posttraining data showed significant improvements (pretraining mean [SD] versus posttraining mean [SD]; P value) in HF knowledge (11.21 [1.90] versus 12.21 [1.85]; P =0.0000) and HF caregiving self-efficacy (75.21 [16.57] versus 82.29 [16.49]; P =0.0017); the greatest gains occurred among those with the lowest pre-training scores. Participants found the course engaging, technically feasible, and highly relevant to their scope of care. Conclusions: We developed and piloted the first HF training course for HHWs, which was feasible, acceptable, and improved their HF knowledge and caregiving self-efficacy. Our findings warrant scalability to the workforce at large with a train-the-trainer model.