TrackMate: An open and extensible platform for single-particle tracking.

We present TrackMate, an open source Fiji plugin for the automated, semi-automated, and manual tracking of single-particles. It offers a versatile and modular solution that works out of the box for end users, through a simple and intuitive user interface. It is also easily scriptable and adaptable, operating equally well on 1D over time, 2D over time, 3D over time, or other single and multi-channel image variants. TrackMate provides several visualization and analysis tools that aid in assessing the relevance of results. The utility of TrackMate is further enhanced through its ability to be readily customized to meet specific tracking problems. TrackMate is an extensible platform where developers can easily write their own detection, particle linking, visualization or analysis algorithms within the TrackMate environment. This evolving framework provides researchers with the opportunity to quickly develop and optimize new algorithms based on existing TrackMate modules without the need of having to write de novo user interfaces, including visualization, analysis and exporting tools. The current capabilities of TrackMate are presented in the context of three different biological problems. First, we perform Caenorhabditis-elegans lineage analysis to assess how light-induced damage during imaging impairs its early development. Our TrackMate-based lineage analysis indicates the lack of a cell-specific light-sensitive mechanism. Second, we investigate the recruitment of NEMO (NF-κB essential modulator) clusters in fibroblasts after stimulation by the cytokine IL-1 and show that photodamage can generate artifacts in the shape of TrackMate characterized movements that confuse motility analysis. Finally, we validate the use of TrackMate for quantitative lifetime analysis of clathrin-mediated endocytosis in plant cells.

Mammographic Breast Density and Urbanization: Interactions with BMI, Environmental, Lifestyle, and Other Patient Factors.

Mammographic breast density (MBD) is an important imaging biomarker of breast cancer risk, but it has been suggested that increased MBD is not a genuine finding once corrected for age and body mass index (BMI). This study examined the association of various factors, including both residing in and working in the urban setting, with MBD. Questionnaires were completed by 1144 women attending for mammography at the London Breast Institute in 2012-2013. Breast density was assessed with an automated volumetric breast density measurement system (Volpara) and compared with subjective radiologist assessment. Multivariable linear regression was used to model the relationship between MBD and residence in the urban setting as well as working in the urban setting, adjusting for both age and BMI and other menstrual, reproductive, and lifestyle factors. Urban residence was significantly associated with an increasing percent of MBD, but this association became non-significant when adjusted for age and BMI. This was not the case for women who were both residents in the urban setting and still working. Our results suggest that the association between urban women and increased MBD can be partially explained by their lower BMI, but for women still working, there appear to be other contributing factors.

Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data.

PURPOSE: To (a) compare the performance of full-field digital mammography (FFDM), using hard-copy image reading, with that of screen-film mammography (SFM) within a UK screening program (screening once every 3 years) for women aged 50 years or older and (b) conduct a meta-analysis of published findings along with the UK data. MATERIALS AND METHODS: The study complied with the UK National Health Service Central Office for Research Ethics Committee guidelines; informed patient consent was not required, since analysis was carried out retrospectively after data anonymization. Between January 2006 and June 2007, a London population-based screening center performed 8478 FFDM and 31 720 SFM screening examinations, with modality determined by the type of machine available at the screening site. Logistic regression was used to assess whether breast cancer detection rates and recall rates differed between screening modalities. For the meta-analysis, random-effects models were used to combine study-specific estimates, if appropriate. RESULTS: A total of 263 breast cancers were detected. After adjustment for age, ethnicity, area of residence, and type of referral, there was no evidence of differences between FFDM and SFM in terms of detection rates (0.68 [95% confidence interval {CI}: 0.47, 0.89] vs 0.72 [95% CI: 0.58, 0.85], respectively, per 100 screening mammograms; P = .74), recall rates (3.2% [95% CI: 2.8, 3.6] vs 3.4% [95% CI: 3.1, 3.6]; P = .44), positive predictive value (PPV) of an abnormal mammogram, or characteristics of detected tumors. Meta-analysis of data from eight studies showed a slightly higher detection rate for FFDM, particularly at 60 years of age or younger (pooled FFDM-SFM difference: 0.11 [95% CI: 0.04, 0.18] per 100 screening mammograms), but no clear modality differences in recall rates or PPVs. CONCLUSION: Within a routine screening program, FFDM with hard-copy image reading performed as well as SFM in terms of process indicators; the meta-analysis was consistent with FFDM yielding detection rates at least as high as those for SFM.