Computationally efficient cardiac views projection using 3D Convolutional Neural Networks

4D Flow is an MRI sequence which allows acquisition of 3D images of the heart. The data is typically acquired volumetrically, so it must be reformatted to generate cardiac long axis and short axis views for diagnostic interpretation. These views may be generated by placing 6 landmarks: the left and right ventricle apex, and the aortic, mitral, pulmonary, and tricuspid valves. In this paper, we propose an automatic method to localize landmarks in order to compute the cardiac views. Our approach consists of first calculating a bounding box that tightly crops the heart, followed by a landmark localization step within this bounded region. Both steps are based on a 3D extension of the recently introduced ENet. We demonstrate that the long and short axis projections computed with our automated method are of equivalent quality to projections created with landmarks placed by an experienced cardiac radiologist, based on a blinded test administered to a different cardiac radiologist

ScarGAN: Chained Generative Adversarial Networks to Simulate Pathological Tissue on Cardiovascular MR Scans

Medical images with specific pathologies are scarce, but a large amount of data is usually required for a deep convolutional neural network (DCNN) to achieve good accuracy. We consider the problem of segmenting the left ventricular (LV) myocardium on late gadolinium enhancement (LGE) cardiovascular magnetic resonance (CMR) scans of which only some of the scans have scar tissue. We propose ScarGAN to simulate scar tissue on healthy myocardium using chained generative adversarial networks (GAN). Our novel approach factorizes the simulation process into 3 steps: 1) a mask generator to simulate the shape of the scar tissue; 2) a domain-specific heuristic to produce the initial simulated scar tissue from the simulated shape; 3) a refining generator to add details to the simulated scar tissue. Unlike other approaches that generate samples from scratch, we simulate scar tissue on normal scans resulting in highly realistic samples. We show that experienced radiologists are unable to distinguish between real and simulated scar tissue. Training a U-Net with additional scans with scar tissue simulated by ScarGAN increases the percentage of scar pixels correctly included in LV myocardium prediction from 75.9% to 80.5%.Comment: 12 pages, 5 figures. To appear in MICCAI DLMIA 201

Síndrome de Burnout en personal de salud de emergencia del Hospital Central de la Fuerza Aérea del Perú. Febrero-julio 2014

El documento digital no refiere asesor.Establece el grado de presentación del síndrome de Burnout en el personal de salud que labora en el Servicio de Emergencia del Hospital Central de la Fuerza Aérea del Perú en el 2014. El síndrome de Burnout es una afección que consiste en un estrés crónico caracterizado por síntomas de despersonalización, cansancio emocional, abandono de la realización personal; que sufren los profesionales que tienen una atención intensa y prolongada con personas que están en situación de necesidad o dependencia. En esta situación por la naturaleza de su labor, se encuentran los médicos, enfermeras, policías, profesores, asistentes sociales, etc. El personal de salud de emergencia está en contacto con personas que acuden por alguna urgencia o emergencia, estas preocupaciones son transmitidas al personal que labora en esta área y pueden desarrollar el síndrome de Burnout. La presentación de este síndrome se puede ver reflejado en la disminución del rendimiento y motivación en el trabajo, así como un distanciamiento con los pacientes y sus familiares.Trabajo académic

Natural Adversarial Objects

Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce a new dataset, Natural Adversarial Objects (NAO), to evaluate the robustness of object detection models. NAO contains 7,934 images and 9,943 objects that are unmodified and representative of real-world scenarios, but cause state-of-the-art detection models to misclassify with high confidence. The mean average precision (mAP) of EfficientDet-D7 drops 74.5% when evaluated on NAO compared to the standard MSCOCO validation set. Moreover, by comparing a variety of object detection architectures, we find that better performance on MSCOCO validation set does not necessarily translate to better performance on NAO, suggesting that robustness cannot be simply achieved by training a more accurate model. We further investigate why examples in NAO are difficult to detect and classify. Experiments of shuffling image patches reveal that models are overly sensitive to local texture. Additionally, using integrated gradients and background replacement, we find that the detection model is reliant on pixel information within the bounding box, and insensitive to the background context when predicting class labels. NAO can be downloaded at https://drive.google.com/drive/folders/15P8sOWoJku6SSEiHLEts86ORfytGezi8

Synthesis and evaluation of 18F-labeled carbonic anhydrase IX inhibitors for imaging with positron emission tomography

Two carbonic anhydrase IX (CA IX) inhibitors were radiolabeled with (18)F, and evaluated for imaging CA IX expression. Despite good affinity for CA IX and excellent plasma stability, uptake of both tracers in CA IX-expressing HT-29 tumor xenografts in mice was low. (18)F-FEC accumulated predominately in the liver and nasal cavity, whereas a significant amount of (18)F-U-104 was retained in blood. Due to minimal uptake in HT-29 tumors compared to other organs/tissues, these two tracers are not suitable for use for CA IX-targeted imaging

Uncovering Red and Dusty Ultraluminous X-Ray Sources with Spitzer

We present a mid-infrared (IR) sample study of nearby ultraluminous X-ray sources (ULXs) using multiepoch observations with the Infrared Array Camera (IRAC) on the Spitzer Space Telescope. Spitzer/IRAC observations taken after 2014 were obtained as part of the Spitzer Infrared Intensive Transients Survey. Our sample includes 96 ULXs located within 10 Mpc. Of the 96 ULXs, 12 have candidate counterparts consistent with absolute mid-IR magnitudes of supergiants, and 16 counterparts exceeded the mid-IR brightness of single supergiants and are thus more consistent with star clusters or non-ULX background active galactic nuclei. The supergiant candidate counterparts exhibit a bimodal color distribution in a Spitzer/IRAC color–magnitude diagram, where "red" and "'blue" ULXs fall in IRAC colors [3.6] – [4.5] ~ 0.7 and [3.6] – [4.5] ~ 0.0, respectively. The mid-IR colors and absolute magnitudes of four "red" and five "blue" ULXs are consistent with those of supergiant B[e] (sgB[e]) and red supergiant (RSG) stars, respectively. Although "blue," RSG-like mid-IR ULX counterparts likely host RSG mass donors; we propose that "red" counterparts are ULXs exhibiting the "B[e] phenomenon" rather than hosts of sgB[e] mass donors. We show that the mid-IR excess from the "red" ULXs is likely due to thermal emission from circumstellar or circumbinary dust. Using dust as a probe for total mass, we estimate mass-loss rates of Ṁ ~ 1 x 10^(-4) M_⊙ yr^(−1) in dust-forming outflows of red ULXs. Based on the transient mid-IR behavior and its relatively flat spectral index, α = −0.19 ± 0.1, we suggest that the mid-IR emission from Holmberg IX X-1 originates from a variable jet

The Recent Volcanic History of Axial Seamount: Geophysical Insights into Past Eruption Dynamics with an Eye Toward Enhanced Observations of Future Eruptions

To understand the processes that form oceanic crust as well as the role of submarine volcanoes in exchanging heat and chemicals with the ocean and in supporting chemosynthetic biological communities, it is essential to study underwater eruptions. The world’s most advanced underwater volcano observatory—the Ocean Observatories Initiative Cabled Array at Axial Seamount—builds upon ~30 years of sustained geophysical monitoring at this site with autonomous and remote systems. In April 2015, only months after the Cabled Array’s installation, it recorded an eruption at Axial Seamount, adding to the records of two prior eruptions in 1998 and 2011. Between eruptions, magma recharge is focused beneath the southeast part of the summit caldera, leading to steady inflation and increasing rates of seismicity. During each eruption, the volcano deflates over days to weeks, coincident with high levels of seismicity as a dike is emplaced along one of the volcano’s rifts and lava erupts on the seafloor. Cabled Array seismic data show that motions on an outward-dipping ring fault beneath the caldera accommodate the inflation and deflation. Eruptions appear to occur at a predictable level of inflation; hence, it should be possible to time deployments of additional cabled and autonomous instrumentation to further enhance observations of the next eruption