Representation of sharp rifts and faults mechanics in modeling ice shelf flow dynamics: Application to Brunt/Stancomb-Wills Ice Shelf, Antarctica

Ice shelves play a major role in buttressing ice sheet flow into the ocean, hence the importance of accurate numerical modeling of their stress regime. Commonly used ice flow models assume a continuous medium and are therefore complicated by the presence of rupture features (crevasses, rifts, and faults) that significantly affect the overall flow patterns. Here we apply contact mechanics and penalty methods to develop a new ice shelf flow model that captures the impact of rifts and faults on the rheology and stress distribution of ice shelves. The model achieves a best fit solution to satellite observations of ice shelf velocities to infer the following: (1) a spatial distribution of contact and friction points along detected faults and rifts, (2) a more realistic spatial pattern of ice shelf rheology, and (3) a better representation of the stress balance in the immediate vicinity of faults and rifts. Thus, applying the model to the Brunt/Stancomb-Wills Ice Shelf, Antarctica, we quantify the state of friction inside faults and the opening rates of rifts and obtain an ice shelf rheology that remains relatively constant everywhere else on the ice shelf. We further demonstrate that better stress representation has widespread application in examining aspects affecting ice shelf structure and dynamics including the extent of ice mélange in rifts and the change in fracture configurations. All are major applications for better insight into the important question of ice shelf stability

Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac

Ultrasound imaging is one of the most widely used multipurpose imaging modalities for monitoring and diagnosing early pregnancy events. The first sign and measurable element of an early pregnancy is the appearance of the Gestational Sac (GS). Currently, the size of the GS is manually estimated from ultrasound images. The manual measurements tend to result in inter- and intraobserver variations, which may lead to difficulties in diagnosis. This paper proposes a new method for automatic identification of miscarriage cases in the first trimester of pregnancy. The proposed method automatically segments the GS and calculates the Mean Sac Diameter (MSD) and other geometric features of the segmented sac. After classifying the image based on the extracted features into either a pregnancy of unknown viability (PUV) or a possible miscarriage case, we assign the decision with a strength level to reflect its reliability. The paper argues that the level of decision strength gives more insight into decision making than other classical alternatives and makes the automated decision process closer to the diagnosis practice by exper

Automated characterisation of ultrasound images of ovarian tumours: the diagnostic accuracy of a support vector machine and image processing with a local binary pattern operator

Introduction: Preoperative characterisation of ovarian masses into benign or malignant is of paramount importance to optimise patient management. Objectives: In this study, we developed and validated a computerised model to characterise ovarian masses as benign or malignant. Materials and methods: Transvaginal 2D B mode static ultrasound images of 187 ovarian masses with known histological diagnosis were included. Images were first pre-processed and enhanced, and Local Binary Pattern Histograms were then extracted from 2 × 2 blocks of each image. A Support Vector Machine (SVM) was trained using stratified cross validation with randomised sampling. The process was repeated 15 times and in each round 100 images were randomly selected. Results: The SVM classified the original non-treated static images as benign or malignant masses with an average accuracy of 0.62 (95% CI: 0.59-0.65). This performance significantly improved to an average accuracy of 0.77 (95% CI: 0.75-0.79) when images were pre-processed, enhanced and treated with a Local Binary Pattern operator (mean difference 0.15: 95% 0.11-0.19, p < 0.0001, two-tailed t test). Conclusion: We have shown that an SVM can classify static 2D B mode ultrasound images of ovarian masses into benign and malignant categories. The accuracy improves if texture related LBP features extracted from the images are considered

Sensitivity of the dynamics of Pine Island Glacier, West Antarctica, to climate forcing for the next 50 years

Pine Island Glacier, a major contributor to sea level rise in West Antarctica, has been undergoing significant changes over the last few decades. Here, we employ a three-dimensional, higher-order model to simulate its evolution over the next 50 yr in response to changes in its surface mass balance, the position of its calving front and ocean-induced ice shelf melting. Simulations show that the largest climatic impact on ice dynamics is the rate of ice shelf melting, which rapidly affects the glacier speed over several hundreds of kilometers upstream of the grounding line. Our simulations show that the speedup observed in the 1990s and 2000s is consistent with an increase in sub-ice-shelf melting. According to our modeling results, even if the grounding line stabilizes for a few decades, we find that the glacier reaction can continue for several decades longer. Furthermore, Pine Island Glacier will continue to change rapidly over the coming decades and remain a major contributor to sea level rise, even if ocean-induced melting is reduced

Evaluation of machine learning methods with Fourier Transform features for classifying ovarian tumors based on ultrasound images

Introduction: Ovarian tumors are the most common diagnostic challenge for gynecologists and ultrasound examination has become the main technique for assessment of ovarian pathology and for preoperative distinction between malignant and benign ovarian tumors. However, ultrasonography is highly examiner-dependent and there may be an important variability between two different specialists when examining the same case. The objective of this work is the evaluation of different well-known Machine Learning (ML) systems to perform the automatic categorization of ovarian tumors from ultrasound images. Methods: We have used a real patient database whose input features have been extracted from 348 images, from the IOTA tumor images database, holding together with the class labels of the images. For each patient case and ultrasound image, its input features have been previously extracted using Fourier descriptors computed on the Region Of Interest (ROI). Then, four ML techniques are considered for performing the classification stage: K-Nearest Neighbors (KNN), Linear Discriminant (LD), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). Results: According to our obtained results, the KNN classifier provides inaccurate predictions (less than 60% of accuracy) independently of the size of the local approximation, whereas the classifiers based on LD, SVM and ELM are robust in this biomedical classification (more than 85% of accuracy). Conclusions: ML methods can be efficiently used for developing the classification stage in computer-aided diagnosis systems of ovarian tumor from ultrasound images. These approaches are able to provide automatic classification with a high rate of accuracy. Future work should aim at enhancing the classifier design using ensemble techniques. Another ongoing work is to exploit different kind of features extracted from ultrasound images

Mechanisms driving variability in the ocean forcing of Pine Island Glacier

Pine Island Glacier (PIG) terminates in a rapidly melting ice shelf, and ocean circulation and temperature are implicated in the retreat and growing contribution to sea level rise of PIG and nearby glaciers. However, the variability of the ocean forcing of PIG has been poorly constrained due to a lack of multi-year observations. Here we show, using a unique record close to the Pine Island Ice Shelf (PIIS), that there is considerable oceanic variability at seasonal and interannual timescales, including a pronounced cold period from October 2011 to May 2013. This variability can be largely explained by two processes: cumulative ocean surface heat fluxes and sea ice formation close to PIIS; and interannual reversals in ocean currents and associated heat transport within Pine Island Bay, driven by a combination of local and remote forcing. Local atmospheric forcing therefore plays an important role in driving oceanic variability close to PIIS

Grounding line retreat of Pope, Smith, and Kohler Glaciers, West Antarctica, measured with Sentinel-1a radar interferometry data

We employ Sentinel-1a C band satellite radar interferometry data in Terrain Observation with Progressive Scans mode to map the grounding line and ice velocity of Pope, Smith, and Kohler glaciers, in West Antarctica, for the years 2014–2016 and compare the results with those obtained using Earth Remote Sensing Satellites (ERS-1/2) in 1992, 1996, and 2011. We observe an ongoing, rapid grounding line retreat of Smith at 2&nbsp;km/yr (40&nbsp;km since 1996), an 11&nbsp;km retreat of Pope (0.5&nbsp;km/yr), and a 2&nbsp;km readvance of Kohler since 2011. The variability in glacier retreat is consistent with the distribution of basal slopes, i.e., fast along retrograde beds and slow along prograde beds. We find that several pinning points holding Dotson and Crosson ice shelves disappeared since 1996 due to ice shelf thinning, which signal the ongoing weakening of these ice shelves. Overall, the results indicate that ice shelf and glacier retreat in this sector remain unabated

Mass balance reassessment of glaciers draining into the Abbot and Getz Ice Shelves of West Antarctica

We present a reassessment of input-output method ice mass budget estimates for the Abbot and Getz regions of West Antarctica using CryoSat-2-derived ice thickness estimates. The mass budget is 8 ± 6 Gt yr−1 and 5 ± 17 Gt yr−1 for the Abbot and Getz sectors, respectively, for the period 2006–2008. Over the Abbot region, our results resolve a previous discrepancy with elevation rates from altimetry, due to a previous 30% overestimation of ice thickness. For the Getz sector, our results are at the more positive bound of estimates from other techniques. Grounding line velocity increases up to 20% between 2007 and 2014 alongside mean elevation rates of −0.67 ± 0.13 m yr−1 between 2010 and 2013 indicate the onset of a dynamic thinning signal. Mean snowfall trends of −0.33 m yr−1 water equivalent since 2006 indicate recent mass trends are driven by both ice dynamics and surface processes

West Antarctic Ice Sheet retreat in the Amundsen Sea driven by decadal oceanic variability

Mass loss from the Amundsen Sea sector of the West Antarctic Ice Sheet has increased in recent decades, suggestive of sustained ocean forcing or an ongoing, possibly unstable, response to a past climate anomaly. Lengthening satellite records appear to be incompatible with either process, however, revealing both periodic hiatuses in acceleration and intermittent episodes of thinning. Here we use ocean temperature, salinity, dissolved-oxygen and current measurements taken from 2000 to 2016 near the Dotson Ice Shelf to determine temporal changes in net basal melting. A decadal cycle dominates the ocean record, with melt changing by a factor of about four between cool and warm extremes via a nonlinear relationship with ocean temperature. A warm phase that peaked around 2009 coincided with ice-shelf thinning and retreat of the grounding line, which re-advanced during a post-2011 cool phase. These observations demonstrate how discontinuous ice retreat is linked with ocean variability, and that the strength and timing of decadal extremes is more influential than changes in the longer-term mean state. The nonlinear response of melting to temperature change heightens the sensitivity of Amundsen Sea ice shelves to such variability, possibly explaining the vulnerability of the ice sheet in that sector, where subsurface ocean temperatures are relatively high