Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans

Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19

Correlations between stacked structures and weak itinerant magnetic properties of La2−x_{2-x} Yx_x Ni7_7 compounds

Hexagonal La$_2$Ni$_7$ and rhombohedral Y$_2$Ni$_7$ are weak itinerant antiferromagnet (wAFM) and ferromagnet (wFM), respectively. The crystal structure and magnetic properties of $A_2B_7$ intermetallic compounds ($A$ = La, Y, $B$ = Ni) have been investigated combining X-ray powder diffraction and magnetic measurements. The La$_{2-x}$Y$_x$Ni$_7$ intermetallic compounds with $0 \leq x \leq 1$ crystallize in the Ce$_{2}$Ni$_7$-type hexagonal structure with Y preferentially located in the [$AB_2$] units. The compounds with larger Y content ($1.2 \leq x < 2$) crystallize in both hexagonal and rhombohedral (Gd$_2$Co$_7$-type) structures with a progressive substitution of Y for La in the $A$ sites belonging to the [$AB_5$] units. Y$_2$Ni$_7$ crystallizes in the rhombohedral structure only. The average cell volume decreases linearly versus Y content, whereas the c/a ratio presents a minimum at $x = 1$ due to geometric constrains. The magnetic properties are strongly dependent on the structure type and the Y content. La$_{2}$Ni$_7$ displays a complex metamagnetic behavior with split AFM peaks. Compounds with x = 0.25 and 0.5 display a wAFM ground state and two metamagnetic transitions, the first one towards an intermediate wAFM state and the second one towards a FM state.T$_N$ and the second critical field increase with the Y content, indicating a stabilization of the AFM state. LaYNi$_7$, which is as the boundary between the two structure types, presents a very wFM state at low field and an AFM state as the applied field increases. All the compounds with $x > 1$ and containing a rhombohedral phase are wFM with $T_C$ = 53(2) K. In addition to the experimental studies, first principles calculations using spin polarization have been performed to interpret the evolution of both structural phase stability and magnetic ordering for $0 \leq x < 2$.Comment: 26 pages (7 for supplementary material), 4 tables, 9 main figures and 8 figures in supplementary materia

Emphysema subtyping on thoracic computed tomography scans using deep neural networks

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.</p

Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society's visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52\%, outperforming a previously published method's accuracy of 45\%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes

Emphysema subtyping on thoracic computed tomography scans using deep neural networks

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.</p

Emphysema subtyping on thoracic computed tomography scans using deep neural networks

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.</p

Emphysema subtyping on thoracic computed tomography scans using deep neural networks

Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.</p

The Mass-Concentration Relation and the Stellar-to-Halo Mass Ratio in the CFHT Stripe 82 Survey

We present a new measurement of the mass-concentration relation and the stellar-to-halo mass ratio over the halo mass range $5\times 10^{12}$ to $2\times 10^{14}M_{\odot}$. To achieve this, we use weak lensing measurements from the CFHT Stripe 82 Survey (CS82), combined with the central galaxies from the redMaPPer cluster catalogue and the LOWZ/CMASS galaxy sample of the Sloan Digital Sky Survey-III Baryon Oscillation Spectroscopic Survey Tenth Data Release. The stacked lensing signals around these samples are modelled as a sum of contributions from the central galaxy, its dark matter halo, and the neighboring halos, as well as a term for possible centering errors. We measure the mass-concentration relation: $c_{200c}(M)=A(\frac{M_{200c}}{M_0})^{B}$ with $A=5.24\pm1.24, B=-0.13\pm0.10$ for $0.2<z<0.4$ and $A=6.61\pm0.75, B=-0.15\pm0.05$ for $0.4<z<0.6$. These amplitudes and slopes are completely consistent with predictions from recent simulations. We also measure the stellar-to-halo mass ratio for our samples, and find results consistent with previous measurements from lensing and other techniques.Comment: 10 pages, 3 figures, 3 table

Chartis Measurement of Collateral Ventilation:Conscious Sedation versus General Anesthesia

BACKGROUND: Absence of interlobar collateral ventilation using the Chartis measurement is the key predictor for successful endobronchial valve treatment in severe emphysema. Chartis was originally validated in spontaneous breathing patients under conscious sedation (CS); however, this can be challenging due to cough, mucus secretion, mucosal swelling, and bronchoconstriction. Performing Chartis under general anesthesia (GA) avoids these problems and may result in an easier procedure with a higher success rate. However, using Chartis under GA with positive pressure ventilation has not been validated. OBJECTIVES: In this study we investigated the impact of anesthesia technique, CS versus GA, on the feasibility and outcomes of Chartis measurement. METHODS: We retrospectively analyzed all Chartis measurements performed at our hospital from October 2010 until December 2017. RESULTS: We analyzed 250 emphysema patients (median forced expiratory volume in 1 s 26%, range 12-52% predicted). In 121 patients (48%) the measurement was performed using CS, in 124 (50%) using GA, and in 5 (2%) both anesthesia techniques were used. In total, 746 Chartis readings were analyzed (432 CS, 277 GA, and 37 combination). Testing under CS took significantly longer than GA (median 19 min [range 5-65] vs. 11 min [3-35], p < 0.001) and required more measurements (3 [1-13] vs. 2 [1-6], p < 0.001). There was no significant difference in target lobe volume reduction after treatment (-1,123 mL [-3,604 to 332] in CS vs. -1,251 mL [-3,333 to -1] in GA, p = 0.35). CONCLUSIONS: In conclusion, Chartis measurement under CS took significantly longer and required more measurements than under GA, without a difference in treatment outcome. We recommend a prospective trial comparing both techniques within the same patients to validate this approach

Cosmological constraints from weak lensing peak statistics with Canada-France-Hawaii Telescope Stripe 82 Survey

We derived constraints on cosmological parameters using weak lensing peak statistics measured on the ∼ 130 deg2 of the Canada-France-Hawaii Telescope Stripe 82 Survey. This analysis demonstrates the feasibility of using peak statistics in cosmological studies. For our measurements, we considered peaks with signal-to-noise ratio in the range of ν = [3, 6]. For a flat Λ cold dark matter model with only (Ωm, σ8) as free parameters, we constrained the parameters of the following relation Σ8 = σ8(Ωm/0.27)α to be Σ8 = 0.82 ± 0.03 and α = 0.43 ± 0.02. The α value found is considerably smaller than the one measured in two-point and three-point cosmic shear correlation analyses, showing a significant complement of peak statistics to standard weak lensing cosmological studies. The derived constraints on (Ωm, σ8) are fully consistent with the ones from either WMAP9 or Planck. From the weak lensing peak abundances alone, we obtained marginalized mean values of $\Omega _{\rm m}=0.38^{+0.27}_{-0.24}$ and σ8 = 0.81 ± 0.26. Finally, we also explored the potential of using weak lensing peak statistics to constrain the mass-concentration relation of dark matter haloes simultaneously with cosmological parameter