Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas

The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function. Cardiac motion atlases provide a space of reference in which the motion fields of a cohort of subjects can be directly compared. In this work, a cardiac motion atlas is built from cine MR data from the UK Biobank (~ 6000 subjects). Two automated quality control strategies are proposed to reject subjects with insufficient image quality. Based on the atlas, three dimensionality reduction algorithms are evaluated to learn data-driven cardiac motion descriptors, and statistical methods used to study the association between these descriptors and non-imaging data. Results show a positive correlation between the atlas motion descriptors and body fat percentage, basal metabolic rate, hypertension, smoking status and alcohol intake frequency. The proposed method outperforms the ability to identify changes in cardiac function due to these known cardiovascular risk factors compared to ejection fraction, the most commonly used descriptor of cardiac function. In conclusion, this work represents a framework for further investigation of the factors influencing cardiac health.Comment: 2018 International Workshop on Statistical Atlases and Computational Modeling of the Hear

Localization Recall Precision (LRP): A New Performance Metric for Object Detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose 'Localization Recall Precision (LRP) Error', a new metric which we specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the 'Optimal LRP', the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, Optimal LRP determines the 'best' confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that, for state-of-the-art object (SOTA) detectors, Optimal LRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector which uses a SOTA still image object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. At https://github.com/cancam/LRP we provide the source code that can compute LRP for the PASCAL VOC and MSCOCO datasets. Our source code can easily be adapted to other datasets as well.Comment: to appear in ECCV 201

Segment, Select, Correct: A Framework for Weakly-Supervised Referring Segmentation

Referring Image Segmentation (RIS) - the problem of identifying objects in images through natural language sentences - is a challenging task currently mostly solved through supervised learning. However, while collecting referred annotation masks is a time-consuming process, the few existing weakly-supervised and zero-shot approaches fall significantly short in performance compared to fully-supervised learning ones. To bridge the performance gap without mask annotations, we propose a novel weakly-supervised framework that tackles RIS by decomposing it into three steps: obtaining instance masks for the object mentioned in the referencing instruction (segment), using zero-shot learning to select a potentially correct mask for the given instruction (select), and bootstrapping a model which allows for fixing the mistakes of zero-shot selection (correct). In our experiments, using only the first two steps (zero-shot segment and select) outperforms other zero-shot baselines by as much as 19%, while our full method improves upon this much stronger baseline and sets the new state-of-the-art for weakly-supervised RIS, reducing the gap between the weakly-supervised and fully-supervised methods in some cases from around 33% to as little as 14%. Code is available at https://github.com/fgirbal/segment-select-correct

Recurrence patterns of locally advanced head and neck squamous cell carcinoma after 3D conformal (chemo)-radiotherapy

<p>Abstract</p> <p>Background</p> <p>To establish recurrence patterns among locally advanced head and neck non-nasopharyngeal squamous cell carcinoma (HNSCC) patients treated with radical (chemo-) radiotherapy and to correlate the sites of loco-regional recurrence with radiotherapy doses and target volumes</p> <p>Method</p> <p>151 locally advanced HNSCC patients were treated between 2004-2005 using radical three-dimensional conformal radiotherapy. Patients with prior surgery to the primary tumour site were excluded. The sites of locoregional relapses were correlated with radiotherapy plans by the radiologist and a planning dosimetrist.</p> <p>Results</p> <p>Median age was 59 years (range:34-89). 35 patients had stage III disease, 116 patients had stage IV A/B. 36 patients were treated with radiotherapy alone, 42 with induction chemotherapy, 63 with induction and concomitant chemoradiotherapy and 10 concomitant chemoradiotherapy. Median follow-up was 38 months (range 3-62). 3-year cause specific survival was 66.8%. 125 of 151 (82.8%) achieved a complete response to treatment. Amongst these 125 there were 20 local-regional recurrence, comprising 8 local, 5 regional and 7 simultaneous local and regional; synchronous distant metastases occurred in 7 of the 20. 9 patients developed distant metastases in the absence of locoregional failure. For the 14 local recurrences with planning data available, 12 were in-field, 1 was marginal, and 1 was out-of-field. Of the 11 regional failures with planning data available, 7 were in-field, 1 was marginal and 3 were out-of-field recurrences.</p> <p>Conclusion</p> <p>The majority of failures following non-surgical treatment for locally advanced HNSCC were loco-regional, within the radiotherapy target volume. Improving locoregional control remains a high priority.</p

Localization recall precision (LRP): A new performance metric for object detection

Average precision (AP), the area under the recall-precision (RP) curve, is the standard performance measure for object detection. Despite its wide acceptance, it has a number of shortcomings, the most important of which are (i) the inability to distinguish very different RP curves, and (ii) the lack of directly measuring bounding box localization accuracy. In this paper, we propose “Localization Recall Precision (LRP) Error”, a new metric specifically designed for object detection. LRP Error is composed of three components related to localization, false negative (FN) rate and false positive (FP) rate. Based on LRP, we introduce the “Optimal LRP” (oLRP), the minimum achievable LRP error representing the best achievable configuration of the detector in terms of recall-precision and the tightness of the boxes. In contrast to AP, which considers precisions over the entire recall domain, oLRP determines the “best” confidence score threshold for a class, which balances the trade-off between localization and recall-precision. In our experiments, we show that oLRP provides richer and more discriminative information than AP. We also demonstrate that the best confidence score thresholds vary significantly among classes and detectors. Moreover, we present LRP results of a simple online video object detector and show that the class-specific optimized thresholds increase the accuracy against the common approach of using a general threshold for all classes. Our experiments demonstrate that LRP is more competent than AP in capturing the performance of detectors. Our source code for PASCAL VOC AND MSCOCO datasets are provided at https://github.com/cancam/LRP

The contribution of small shrubby patches to the functional diversity of wood-pastures

Wood-pastures are grazed systems resulting from a long-term use of natural woodlands by humans. These social-ecological systems, covering vast areas of Europe and other temperate regions, have both high biodiversity and economic values, so many are classified as High Nature Value Farmlands. However, in some regions a loss of spatial heterogeneity threatens this natural value. We investigated the potential contribution of tiny shrubby patches to increase spatial heterogeneity and functional diversity in wood-pasture landscapes. Specifically, we compared functional composition (Community Weighted Means) and functional diversity (Functional Dispersion and Functional Evenness) of assemblages of plants, beetles and lichens in those patches (252-3000 m(2)) and in the wood-pasture matrix. We found that shrubby patches and matrix harbour species assemblages with very distinct functional compositions in all studied taxonomic groups. Evergreen, woody, broad-leafed and fleshy-fruited are better represented in the patches. In beetles, the main differences were a greater prevalence of small-sized and fungivore species in the patches. Shrubby patches also mostly harboured lichens with fruticose- and foliose-broad growth forms, a greater humidity preference, and lower eutrophication tolerance. Moreover, the two indexes used to quantify functional diversity (Functional Dispersion and Functional Evenness) show that, overall, diversity is greater in patches than in the matrix; in patches Functional Dispersion is statistically higher for plants, and Evenness is statistically higher for beetles and lichen. These differences are all consistent with the very distinct ecological conditions in the matrix and patches. The greater overall functional diversity of shrubby patches, and the major differences in functional composition between patches and matrix, observed for all taxa, indicate that these patches greatly enhance the functional diversity of species assemblages in wood-pasture landscapes. Consequently, preserving and promoting tiny shrubby patches is a potentially valuable low-cost management tool to increase biodiversity and improve ecosystem functioning in wood-pasture landscapes.Peer reviewe

Help-seeking behaviors for female sexual dysfunction: a cross sectional study from Iran

<p>Abstract</p> <p>Background</p> <p>Female sexual dysfunctions (FSD) are prevalent multifactor problems that in general remain misdiagnosed in primary health care. This population-based study investigated help-seeking behaviors among women with FSD in Iran.</p> <p>Methods</p> <p>This was a cross sectional study carried out in Kohgilouyeh-Boyer-Ahmad province in Iran. Using quota sampling all sexually active women aged 15 and over registered in primary health care delivery centers were studied. Experience of sexual problems was assessed using an ad-hoc questionnaire (Female sexual dysfunction: help-seeking behaviors survey) containing 14 items. Trained female nurses interviewed all participants after a verbal informed consent. Data were analyzed in a descriptive manner.</p> <p>Results</p> <p>In all 1540 women were studied. Of these, 786 (51%) cases had experienced at least one of the FSD problems. Results showed that 35.8% of women with FSD had sought no professional help and the most reasons for not seeking help were identified as: 'time constraints' and believing that it 'did not occur to me' (39.1 and 28.5% respectively). Sixty one percent of women who sought help for FSD reported that 'doctor gave me a definite diagnosis' and 'a definite treatment plan was given' in 57% of cases.</p> <p>Conclusion</p> <p>The study findings indicated that FSD problems were prevalent and many women did not seek help for their problem. Finding 'time constraints' and believing that the problem 'did not occur to me' as the most cited reasons for not seeking help might facilitate to understand potential barriers that exist in recognition and treatment of the female sexual dysfunctions. Since FSD might have a negative impact on interpersonal relationships and women's quality of life, it seems that there is need to address the problem both at local and national primary health care services.</p

Mental health and behaviour of students of public health and their correlation with social support: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Future public health professionals are especially important among students partly because their credibility in light of their professional messages and activities will be tested daily by their clients; and partly because health professionals' own lifestyle habits influence their attitudes and professional activities. A better understanding of public health students' health and its determinants is necessary for improving counselling services and tailoring them to demand. Our aim was to survey public health students' health status and behaviour with a focus on mental health.</p> <p>Methods</p> <p>A cross-sectional study was carried out among public health students at 1-5-years (<it>N </it>= 194) with a self-administered questionnaire that included standardized items on demographic data, mental wellbeing characterized by sense of coherence (SoC) and psychological morbidity, as well as health behaviour and social support. Correlations between social support and the variables for mental health, health status and health behaviour were characterized by pairwise correlation.</p> <p>Results</p> <p>The response rate was 75% and represented students by study year, sex and age in the Faculty. Nearly half of the students were non-smokers, more than one quarter smoked daily. Almost one-fifth of the students suffered from notable psychological distress. The proportion of these students decreased from year 1 to 5. The mean score for SoC was 60.1 and showed an increasing trend during the academic years. 29% of the students lacked social support from their student peers. Significant positive correlation was revealed between social support and variables for mental health. Psychological distress was greater among female public health students than in the same age female group of the general population; whereas the lack of social support was a more prevalent problem among male students.</p> <p>Conclusions</p> <p>Health status and behaviour of public health students is similar to their non-students peers except for their worse mental health. Future public health professionals should be better prepared for coping with the challenges they face during their studies. Universities must facilitate this process by providing helping services targeted at those with highest risk, and developing training to improve coping skills. Social support is also a potentially amenable determinant of mental health during higher education.</p