More Like this

1. Robust segmentation of lung in chest x-ray: applications in analysis of acute respiratory distress syndrome

   https://doi.org/10.1186/s12880-020-00514-y  

   Reamaroon, Narathip; Sjoding, Michael W.; Derksen, Harm; Sabeti, Elyas; Gryak, Jonathan; Barbaro, Ryan P.; Athey, Brian D.; Najarian, Kayvan (December 2020, BMC Medical Imaging)

   null (Ed.)

   Abstract Background This study outlines an image processing algorithm for accurate and consistent lung segmentation in chest radiographs of critically ill adults and children typically obscured by medical equipment. In particular, this work focuses on applications in analysis of acute respiratory distress syndrome – a critical illness with a mortality rate of 40% that affects 200,000 patients in the United States and 3 million globally each year. Methods Chest radiographs were obtained from critically ill adults (n = 100), adults diagnosed with acute respiratory distress syndrome (ARDS) (n = 25), and children (n = 100) hospitalized at Michigan Medicine. Physicians annotated the lung field of each radiograph to establish the ground truth. A Total Variation-based Active Contour (TVAC) lung segmentation algorithm was developed and compared to multiple state-of-the-art methods including a deep learning model (U-Net), a random walker algorithm, and an active spline model, using the Sørensen–Dice coefficient to measure segmentation accuracy. Results The TVAC algorithm accurately segmented lung fields in all patients in the study. For the adult cohort, an averaged Dice coefficient of 0.86 ±0.04 (min: 0.76) was reported for TVAC, 0.89 ±0.12 (min: 0.01) for U-Net, 0.74 ±0.19 (min: 0.15) for the random walker algorithm, and 0.64 ±0.17 (min: 0.20) for the active spline model. For the pediatric cohort, a Dice coefficient of 0.85 ±0.04 (min: 0.75) was reported for TVAC, 0.87 ±0.09 (min: 0.56) for U-Net, 0.67 ±0.18 (min: 0.18) for the random walker algorithm, and 0.61 ±0.18 (min: 0.18) for the active spline model. Conclusion The proposed algorithm demonstrates the most consistent performance of all segmentation methods tested. These results suggest that TVAC can accurately identify lung fields in chest radiographs in critically ill adults and children. 

   more » « less
2. Ionospheric Topside Diffusive Flux and the Formation of Summer Nighttime Ionospheric Electron Density Enhancement Over Millstone Hill

   https://doi.org/10.1029/2021GL097651  

   Cai, Yihui; Yue, Xinan; Wang, Wenbin; Zhang, Shun‐Rong; Liu, Huixin; Lei, Jiuhou; Ren, Zhipeng; Chen, Yiding; Ding, Feng; Ren, Dexin (February 2022, Geophysical Research Letters)

   Abstract Ionospheric F‐region electron density is anomalously higher in the evening than during the daytime on many occasions in the summer in geomagnetic mid‐latitude regions. This unexpected ionospheric diurnal variation has been studied for several decades. The underlying processes have been suggested to be related to meridional winds, topside influx arising from sunset ionospheric collapse, and other factors. However, substantial controversies remain unresolved. Using a numerical model driven by the statistical topsideO⁺diffusive flux from the Millstone Hill incoherent scatter radar data, we provide new insight into the competing roles of topside diffusive flux, neutral winds, and electric fields in forming the evening density peak. Simulations indicate that while meridional winds, which turn equatorward before sunset, are essential to sustain the daytime ionization near dusk, the topside diffusive flux is critically important for the formation and timing of the summer evening density peak. 

   more » « less
3. Multiple-scattering simulator-trained neural network for intensity diffraction tomography

   https://doi.org/10.1364/OE.477396  

   Matlock, Alex; Zhu, Jiabei; Tian, Lei (January 2023, Optics Express)

   Recovering 3D phase features of complex biological samples traditionally sacrifices computational efficiency and processing time for physical model accuracy and reconstruction quality. Here, we overcome this challenge using an approximant-guided deep learning framework in a high-speed intensity diffraction tomography system. Applying a physics model simulator-based learning strategy trained entirely on natural image datasets, we show our network can robustly reconstruct complex 3D biological samples. To achieve highly efficient training and prediction, we implement a lightweight 2D network structure that utilizes a multi-channel input for encoding the axial information. We demonstrate this framework on experimental measurements of weakly scattering epithelial buccal cells and strongly scatteringC. elegansworms. We benchmark the network’s performance against a state-of-the-art multiple-scattering model-based iterative reconstruction algorithm. We highlight the network’s robustness by reconstructing dynamic samples from a living worm video. We further emphasize the network’s generalization capabilities by recovering algae samples imaged from different experimental setups. To assess the prediction quality, we develop a quantitative evaluation metric to show that our predictions are consistent with both multiple-scattering physics and experimental measurements. 

   more » « less
4. Augmenting astrophysical scaling relations with machine learning: Application to reducing the Sunyaev–Zeldovich flux–mass scatter

   https://doi.org/10.1073/pnas.2202074120  

   Wadekar, Digvijay; Thiele, Leander; Villaescusa-Navarro, Francisco; Hill, J. Colin; Cranmer, Miles; Spergel, David N.; Battaglia, Nicholas; Anglés-Alcázar, Daniel; Hernquist, Lars; Ho, Shirley (March 2023, Proceedings of the National Academy of Sciences)

   Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux−cluster mass relation ( Y SZ − M ), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines Y SZ and concentration of ionized gas ( c gas ): M ∝ Y conc 3/5 ≡ Y SZ 3/5 (1 − A c gas ). Y conc reduces the scatter in the predicted M by ∼20 − 30% for large clusters ( M ≳ 10 14 h −1 M ⊙ ), as compared to using just Y SZ . We show that the dependence on c gas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Y conc on clusters from CAMELS simulations and show that Y conc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4. 

   more » « less
5. The Relationship between IGM Lyα Opacity and Galaxy Density near the End of Reionization

   https://doi.org/10.3847/1538-4357/acf450  

   Christenson, Holly M.; Becker, George D.; D’Aloisio, Anson; Davies, Frederick B.; Zhu, Yongda; Boera, Elisa; Nasir, Fahad; Furlanetto, Steven R.; Malkan, Matthew A. (September 2023, The Astrophysical Journal)

   Abstract Observed scatter in the Lyαopacity of quasar sightlines atz< 6 has motivated measurements of the correlation between Lyαopacity and galaxy density, as models that predict this scatter make strong and sometimes opposite predictions for how they should be related. Our previous work associated two highly opaque Lyαtroughs atz∼ 5.7 with a deficit of Lyαemitting galaxies (LAEs). In this work, we survey two of the most highly transmissive lines of sight at this redshift toward thez= 6.02 quasar SDSS J1306+0356 and thez= 6.17 quasar PSO J359-06. We find that both fields are underdense in LAEs within 10h⁻¹Mpc of the quasar sightline, somewhat less extensive than underdensities associated with Lyαtroughs. We combine our observations with three additional fields from the literature and find that while fields with extreme opacities are generally underdense, moderate opacities span a wider density range. The results at high opacities are consistent with models that invoke UV background fluctuations and/or late reionization to explain the observed scatter in intergalactic medium (IGM) Lyαopacities. There is tension at low opacities, however, as the models tend to associate lower IGM Lyαopacities with higher densities. Although the number of fields surveyed is still small, the low-opacity results may support a scenario in which the ionizing background in low-density regions increases more rapidly than some models suggest after becoming ionized. Elevated gas temperatures from recent reionization may also be making these regions more transparent. 

   more » « less