Deep learning for mass detection in Full Field Digital Mammograms

© 2020 The Authors In recent years, the use of Convolutional Neural Networks (CNNs) in medical imaging has shown improved performance in terms of mass detection and classification compared to current state-of-the-art methods. This paper proposes a fully automated framework to detect masses in Full-Field Digital Mammograms (FFDM). This is based on the Faster Region-based Convolutional Neural Network (Faster-RCNN) model and is applied for detecting masses in the large-scale OPTIMAM Mammography Image Database (OMI-DB), which consists of ∼80,000 FFDMs mainly from Hologic and General Electric (GE) scanners. This research is the first to benchmark the performance of deep learning on OMI-DB. The proposed framework obtained a True Positive Rate (TPR) of 0.93 at 0.78 False Positive per Image (FPI) on FFDMs from the Hologic scanner. Transfer learning is then used in the Faster R-CNN model trained on Hologic images to detect masses in smaller databases containing FFDMs from the GE scanner and another public dataset INbreast (Siemens scanner). The detection framework obtained a TPR of 0.91±0.06 at 1.69 FPI for images from the GE scanner and also showed higher performance compared to state-of-the-art methods on the INbreast dataset, obtaining a TPR of 0.99±0.03 at 1.17 FPI for malignant and 0.85±0.08 at 1.0 FPI for benign masses, showing the potential to be used as part of an advanced CAD system for breast cancer screening

The Zoning of Semi-Enclosed Bodies of Water According to the Sediment Pollution: The Bay of Algeciras as a Case Example

This paper reports a study of the occurrence and levels of polycyclic aromatic hydrocarbons (PAHs) in a bay characterised by a chronic persistent impact. A total of 55 sediment samples were taken at different depths up to 111 m in two sampling campaigns. Chemical analyses were carried out by gas chromatography-mass spectroscopy. The results indicate that: (1) significant spatial variations exist, (2) levels of PAHs are related more strongly to the spatial distribution of sediments than to mineralogy/granulometry, (3) the sediments are slightly-to-moderately contaminated by PAHs, and (4) these PAHs derive from pyrolytic and petrogenic sources. Through use of an innovative data classification system (proposed according to depth and spatial location of sampling points), and using factorial and cluster techniques, five zones have been differentiated depending on the contamination level and source

Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure

We present a study of multiple sclerosis segmentation algorithms conducted at the international MICCAI 2016 challenge. This challenge was operated using a new open-science computing infrastructure. This allowed for the automatic and independent evaluation of a large range of algorithms in a fair and completely automatic manner. This computing infrastructure was used to evaluate thirteen methods of MS lesions segmentation, exploring a broad range of state-of-theart algorithms, against a high-quality database of 53 MS cases coming from four centers following a common definition of the acquisition protocol. Each case was annotated manually by an unprecedented number of seven different experts. Results of the challenge highlighted that automatic algorithms, including the recent machine learning methods (random forests, deep learning, …), are still trailing human expertise on both detection and delineation criteria. In addition, we demonstrate that computing a statistically robust consensus of the algorithms performs closer to human expertise on one score (segmentation) although still trailing on detection scores

Treatment of rats with a self-selected hyperlipidic diet, increases the lipid content of the main adipose tissue sites in a proportion similar to that of the lipids in the rest of organs and tissues

Adipose tissue (AT) is distributed as large differentiated masses, and smaller depots covering vessels, and organs, as well as interspersed within them. The differences between types and size of cells makes AT one of the most disperse and complex organs. Lipid storage is partly shared by other tissues such as muscle and liver. We intended to obtain an approximate estimation of the size of lipid reserves stored outside the main fat depots. Both male and female rats were made overweight by 4-weeks feeding of a cafeteria diet. Total lipid content was analyzed in brain, liver, gastrocnemius muscle, four white AT sites: subcutaneous, perigonadal, retroperitoneal and mesenteric, two brown AT sites (interscapular and perirenal) and in a pool of the rest of organs and tissues (after discarding gut contents). Organ lipid content was estimated and tabulated for each individual rat. Food intake was measured daily. There was a surprisingly high proportion of lipid not accounted for by the main macroscopic AT sites, even when brain, liver and BAT main sites were discounted. Muscle contained about 8% of body lipids, liver 1-1.4%, four white AT sites lipid 28-63% of body lipid, and the rest of the body (including muscle) 38-44%. There was a good correlation between AT lipid and body lipid, but lipid in"other organs" was highly correlated too with body lipid. Brain lipid was not. Irrespective of dietary intake, accumulation of body fat was uniform both for the main lipid storage and handling organs: large masses of AT (but also liver, muscle), as well as in the"rest" of tissues. These storage sites, in specialized (adipose) or not-specialized (liver, muscle) tissues reacted in parallel against a hyperlipidic diet challenge. We postulate that body lipid stores are handled and regulated coordinately, with a more centralized and overall mechanisms than usually assumed

Dissimilar responses of fungal and bacterial communities to soil transplantation simulating abrupt climate changes.

Both fungi and bacteria play essential roles in regulating soil carbon cycling. To predict future carbon stability, it is imperative to understand their responses to environmental changes, which is subject to large uncertainty. As current global warming is causing range shifts toward higher latitudes, we conducted three reciprocal soil transplantation experiments over large transects in 2005 to simulate abrupt climate changes. Six years after soil transplantation, fungal biomass of transplanted soils showed a general pattern of changes from donor sites to destination, which were more obvious in bare fallow soils than in maize cropped soils. Strikingly, fungal community compositions were clustered by sites, demonstrating that fungi of transplanted soils acclimatized to the destination environment. Several fungal taxa displayed sharp changes in relative abundance, including Podospora, Chaetomium, Mortierella and Phialemonium. In contrast, bacterial communities remained largely unchanged. Consistent with the important role of fungi in affecting soil carbon cycling, 8.1%-10.0% of fungal genes encoding carbon-decomposing enzymes were significantly (p < 0.01) increased as compared with those from bacteria (5.7%-8.4%). To explain these observations, we found that fungal occupancy across samples was mainly determined by annual average air temperature and rainfall, whereas bacterial occupancy was more closely related to soil conditions, which remained stable 6 years after soil transplantation. Together, these results demonstrate dissimilar response patterns and resource partitioning between fungi and bacteria, which may have considerable consequences for ecosystem-scale carbon cycling

Current cardiac imaging techniques for detection of left ventricular mass

Estimation of left ventricular (LV) mass has both prognostic and therapeutic value independent of traditional risk factors. Unfortunately, LV mass evaluation has been underestimated in clinical practice. Assessment of LV mass can be performed by a number of imaging modalities. Despite inherent limitations, conventional echocardiography has fundamentally been established as most widely used diagnostic tool. 3-dimensional echocardiography (3DE) is now feasible, fast and accurate for LV mass evaluation. 3DE is also superior to conventional echocardiography in terms of LV mass assessment, especially in patients with abnormal LV geometry. Cardiovascular magnetic resonance (CMR) and cardiovascular computed tomography (CCT) are currently performed for LV mass assessment and also do not depend on cardiac geometry and display 3-dimensional data, as well. Therefore, CMR is being increasingly employed and is at the present standard of reference in the clinical setting. Although each method demonstrates advantages over another, there are also disadvantages to receive attention. Diagnostic accuracy of methods will also be increased with the introduction of more advanced systems. It is also likely that in the coming years new and more accurate diagnostic tests will become available. In particular, CMR and CCT have been intersecting hot topic between cardiology and radiology clinics. Thus, good communication and collaboration between two specialties is required for selection of an appropriate test