Augmenting Auto-context with Global Geometric Features for Spinal Cord Segmentation

Abstract. Anatomical shape variations are typically difficult to model and parametric or hand-crafted models can lead to ill-fitting segmentations. This difficulty can be addressed with a framework like autocontext, that learns to jointly detect and regularize a segmentation. However, mis-segmentation can still occur when a desired structure, such as the spinal cord, has few locally distinct features. High-level knowledge at a global scale (e.g. an MRI contains a single connected spinal cord) is needed to regularize these candidate segmentations. To encode highlevel knowledge, we propose to augment the auto-context framework with global geometric features extracted from the detected candidate shapes. Our classifier then learns these high-level rules and rejects falsely detected shapes. To validate our method we segment the spinal cords from 20 MRI volumes composed of patients with and without multiple sclerosis and demonstrate improvements in accuracy, speed, and manual effort required when compared to state-of-the-art methods.

Predicting the Clinical Management of Skin Lesions Using Deep Learning

Automated machine learning approaches to skin lesion diagnosis from images are approaching dermatologist-level performance. However, current machine learning approaches that suggest management decisions rely on predicting the underlying skin condition to infer a management decision without considering the variability of management decisions that may exist within a single condition. We present the first work to explore image-based prediction of clinical management decisions directly without explicitly predicting the diagnosis. In particular, we use clinical and dermoscopic images of skin lesions along with patient metadata from the Interactive Atlas of Dermoscopy dataset (1011 cases; 20 disease labels; 3 management decisions) and demonstrate that predicting management labels directly is more accurate than predicting the diagnosis and then inferring the management decision (13.73±3.93% and 6.59±2.86% improvement in overall accuracy and AUROC respectively), statistically significant at p<0.001. Directly predicting management decisions also considerably reduces the over-excision rate as compared to management decisions inferred from diagnosis predictions (24.56% fewer cases wrongly predicted to be excised). Furthermore, we show that training a model to also simultaneously predict the seven-point criteria and the diagnosis of skin lesions yields an even higher accuracy (improvements of 4.68±1.89% and 2.24±2.04% in overall accuracy and AUROC respectively) of management predictions. Finally, we demonstrate our model’s generalizability by evaluating on the publicly available MClass-D dataset and show that our model agrees with the clinical management recommendations of 157 dermatologists as much as they agree amongst each other

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Reliability of the Spinal Instability Neoplastic Score (SINS) among radiation oncologists: an assessment of instability secondary to spinal metastases

BACKGROUND: The Spinal Instability Neoplastic Score (SINS) categorizes tumor related spinal instability. It has the potential to streamline the referral of patients with established or potential spinal instability to a spine surgeon. This study aims to define the inter- and intra-observer reliability and validity of SINS among radiation oncologists. METHODS: Thirty-three radiation oncologists, across ten international sites, rated 30 neoplastic spinal disease cases. For each case, the total SINS (0-18 points), three clinical categories (stable: 0-6 points, potentially unstable: 7-12 points, and unstable: 13-18 points), and a binary scale (‘stable’: 0-6 points and ‘current or possible instability’; surgical consultation recommended: 7-18 points) were recorded. Evaluation was repeated 6-8 weeks later. Inter-observer agreement and intra-observer reproducibility were calculated by means of the kappa statistic and translated into levels of agreement (slight, fair, moderate, substantial, and excellent). Validity was determined by comparing the ratings against a spinal surgeon’s consensus standard. RESULTS: Radiation oncologists demonstrated substantial (κ = 0.76) inter-observer and excellent (κ = 0.80) intra-observer reliability when using the SINS binary scale (‘stable’ versus ‘current or possible instability’). Validity of the binary scale was also excellent (κ = 0.85) compared with the gold standard. None of the unstable cases was rated as stable by the radiation oncologists ensuring all were appropriately recommended for surgical consultation. CONCLUSIONS: Among radiation oncologists SINS is a highly reliable, reproducible, and valid assessment tool to address a key question in tumor related spinal disease: Is the spine ‘stable’ or is there ‘current or possible instability’ that warrants surgical assessment

Systematic review of communication technologies to promote access and engagement of young people with diabetes into healthcare

Background: Research has investigated whether communication technologies (e.g. mobile telephony, forums, email) can be used to transfer digital information between healthcare professionals and young people who live with diabetes. The systematic review evaluates the effectiveness and impact of these technologies on communication. Methods: Nine electronic databases were searched. Technologies were described and a narrative synthesis of all studies was undertaken. Results: Of 20,925 publications identified, 19 met the inclusion criteria, with 18 technologies assessed. Five categories of communication technologies were identified: video-and tele-conferencing (n = 2); mobile telephony (n = 3); telephone support (n = 3); novel electronic communication devices for transferring clinical information (n = 10); and web-based discussion boards (n = 1). Ten studies showed a positive improvement in HbA1c following the intervention with four studies reporting detrimental increases in HbA1c levels. In fifteen studies communication technologies increased the frequency of contact between patient and healthcare professional. Findings were inconsistent of an association between improvements in HbA1c and increased contact. Limited evidence was available concerning behavioural and care coordination outcomes, although improvement in quality of life, patientcaregiver interaction, self-care and metabolic transmission were reported for some communication technologies. Conclusions: The breadth of study design and types of technologies reported make the magnitude of benefit and their effects on health difficult to determine. While communication technologies may increase the frequency of contact between patient and health care professional, it remains unclear whether this results in improved outcomes and is often the basis of the intervention itself. Further research is needed to explore the effectiveness and cost effectiveness of increasing the use of communication technologies between young people and healthcare professionals

The ASTRO-H X-ray Observatory

The joint JAXA/NASA ASTRO-H mission is the sixth in a series of highly successful X-ray missions initiated by the Institute of Space and Astronautical Science (ISAS). ASTRO-H will investigate the physics of the high-energy universe via a suite of four instruments, covering a very wide energy range, from 0.3 keV to 600 keV. These instruments include a high-resolution, high-throughput spectrometer sensitive over 0.3-2 keV with high spectral resolution of Delta E < 7 eV, enabled by a micro-calorimeter array located in the focal plane of thin-foil X-ray optics; hard X-ray imaging spectrometers covering 5-80 keV, located in the focal plane of multilayer-coated, focusing hard X-ray mirrors; a wide-field imaging spectrometer sensitive over 0.4-12 keV, with an X-ray CCD camera in the focal plane of a soft X-ray telescope; and a non-focusing Compton-camera type soft gamma-ray detector, sensitive in the 40-600 keV band. The simultaneous broad bandpass, coupled with high spectral resolution, will enable the pursuit of a wide variety of important science themes.Comment: 22 pages, 17 figures, Proceedings of the SPIE Astronomical Instrumentation "Space Telescopes and Instrumentation 2012: Ultraviolet to Gamma Ray

Myeloid Sirtuin 2 expression does not impact long-term Mycobacterium tuberculosis control

Sirtuins (Sirts) regulate several cellular mechanisms through deacetylation of several transcription factors and enzymes. Recently, Sirt2 was shown to prevent the development of inflammatory processes and its expression favors acute Listeria monocytogenes infection. The impact of this molecule in the context of chronic infections remains unknown. We found that specific Sirt2 deletion in the myeloid lineage transiently increased Mycobacterium tuberculosis load in the lungs and liver of conditional mice. Sirt2 did not affect long-term infection since no significant differences were observed in the bacterial burden at days 60 and 120 post-infection. The initial increase in M. tuberculosis growth was not due to differences in inflammatory cell infiltrates in the lung, myeloid or CD4+ T cells. The transcription levels of IFN-?, IL-17, TNF, IL-6 and NOS2 were also not affected in the lungs by Sirt2-myeloid specific deletion. Overall, our results demonstrate that Sirt2 expression has a transitory effect in M. tuberculosis infection. Thus, modulation of Sirt2 activity in vivo is not expected to affect chronic infection with M. tuberculosis.Fundação para a Ciência e Tecnologia, Portugal and cofunded by Programa Operacional Regional do Norte (ON.2–O Novo Norte), Quadro de Referência Estratégico Nacional (QREN), through the Fundo Europeu de Desenvolvimento Regional (FEDER). Project grants: PTDC/SAU-MII/101977/2008 (to AGC) and PTDC/BIA-BCM/102776/2008 (to MS). LMT was supported by FCT Grant SFRH/BPD/77399/20

The genomic basis of adaptive evolution in threespine sticklebacks

Marine stickleback fish have colonized and adapted to thousands of streams and lakes formed since the last ice age, providing an exceptional opportunity to characterize genomic mechanisms underlying repeated ecological adaptation in nature. Here we develop a high-quality reference genome assembly for threespine sticklebacks. By sequencing the genomes of twenty additional individuals from a global set of marine and freshwater populations, we identify a genome-wide set of loci that are consistently associated with marine–freshwater divergence. Our results indicate that reuse of globally shared standing genetic variation, including chromosomal inversions, has an important role in repeated evolution of distinct marine and freshwater sticklebacks, and in the maintenance of divergent ecotypes during early stages of reproductive isolation. Both coding and regulatory changes occur in the set of loci underlying marine–freshwater evolution, but regulatory changes appear to predominate in this well known example of repeated adaptive evolution in nature.National Human Genome Research Institute (U.S.)National Human Genome Research Institute (U.S.) (NHGRI CEGS Grant P50-HG002568