Effect of latent space distribution on the segmentation of images with multiple annotations

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the variation in the reference segmentations for lung tumors and white matter hyperintensities in the brain. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNetComment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2023:005. arXiv admin note: text overlap with arXiv:2207.1287

Calibration techniques for node classification using graph neural networks on medical image data

Miscalibration of deep neural networks (DNNs) can lead to unreliable predictions and hinder their use in clinical decision-making. This miscalibration is often caused by overconfident probability estimates. Calibration techniques such as model ensembles, regularization terms, and post-hoc scaling of the predictions can be employed to improve the calibration performance of DNNs. In contrast to DNNs, graph neural networks (GNNs) tend to exhibit underconfidence. In this study, we investigate the efficacy of calibration techniques developed for DNNs when applied to GNNs trained on medical image data, and compare the calibration performance of binary and multiclass node classification on a benchmark dataset and a medical image dataset. We find that post-hoc methods using Platt scaling or Temperature scaling, or methods that add a regularization term to the loss function during training are most effective to improve calibration. Our results further indicate that these calibration techniques are more effective for multiclass classification tasks compared to binary classification tasks

Influence of learned landmark correspondences on lung CT registration

Background: Disease or injury may cause a change in the biomechanical properties of the lungs, which can alter lung function. Image registration can be used to measure lung ventilation and quantify volume change, which can be a useful diagnostic aid. However, lung registration is a challenging problem because of the variation in deformation along the lungs, sliding motion of the lungs along the ribs, and change in density. Purpose: Landmark correspondences have been used to make deformable image registration robust to large displacements. Methods: To tackle the challenging task of intra-patient lung computed tomography (CT) registration, we extend the landmark correspondence prediction model deep convolutional neural network-Match by introducing a soft mask loss term to encourage landmark correspondences in specific regions and avoid the use of a mask during inference. To produce realistic deformations to train the landmark correspondence model, we use data-driven synthetic transformations. We study the influence of these learned landmark correspondences on lung CT registration by integrating them into intensity-based registration as a distance-based penalty. Results: Our results on the public thoracic CT dataset COPDgene show that using learned landmark correspondences as a soft constraint can reduce median registration error from approximately 5.46 to 4.08 mm compared to standard intensity-based registration, in the absence of lung masks. Conclusions: We show that using landmark correspondences results in minor improvements in local alignment, while significantly improving global alignment

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Extending Probabilistic U-Net Using MC-Dropout to Quantify Data and Model Uncertainty

We extend the Probabilistic U-Net using MC-Dropout to estimate model uncertainty in addition to the data uncertainty in order to improve the overall predictive uncertainty estimate. We use this model on the datasets present in the QUBIQ21 challenge and achieve a mean score of 0.719

Generalized Probabilistic U-Net for Medical Image Segementation

We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net [14] by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations. We study the effect the choice of latent space distribution has on capturing the uncertainty in the reference segmentations using the LIDC-IDRI dataset. We show that the choice of distribution affects the sample diversity of the predictions and their overlap with respect to the reference segmentations. For the LIDC-IDRI dataset, we show that using a mixture of Gaussians results in a statistically significant improvement in the generalized energy distance (GED) metric with respect to the standard Probabilistic U-Net. We have made our implementation available at https://github.com/ishaanb92/GeneralizedProbabilisticUNet

Embryology, diagnosis, and evaluation of congenital hand anomalies

Although congenital hand anomalies are rare, musculoskeletal clinicians should have a basic understanding of their clinical manifestations and the possibility of concurrent anomalies and syndromes. In this review, we provide a brief overview of the embryology of limb development and the molecular pathways involved. We also summarize the clinical manifestations, diagnostic evaluation, and principles of surgical treatment for radial longitudinal deficiency, thumb hypoplasia, ulnar longitudinal deficiency, central deficiency, syndactyly, polydactyly, and amniotic constriction band. Although one of the main goals of treatment is to provide a functional upper extremity, musculoskeletal clinicians should be aware of the clinical findings that should trigger referral to evaluate for life-threatening syndromes

Measurement of prompt D-s(+)-meson production and azimuthal anisotropy in Pb-Pb collisions at root s(NN)=5.02 TeV

The production yield and angular anisotropy of prompt Ds+ mesons were measured as a function of transverse momentum (pT) in Pb–Pb collisions at a centre-of-mass energy per nucleon pair sNN=5.02TeV collected with the ALICE detector at the LHC. Ds+ mesons and their charge conjugates were reconstructed at midrapidity (|y|10GeV/c, the measured Ds+-meson nuclear modification factor RAA is consistent with the one of non-strange D mesons within uncertainties, while at lower pT a hint for a Ds+-meson RAA larger than that of non-strange D mesons is seen. The enhanced production of Ds+ relative to non-strange D mesons is also studied by comparing the pT-dependent Ds+/D0 production yield ratios in Pb–Pb and in pp collisions. The ratio measured in Pb–Pb collisions is found to be on average higher than that in pp collisions in the interval 2<pT<8GeV/c with a significance of 2.3σ and 2.4σ for the 0–10% and 30–50% centrality intervals. The azimuthal anisotropy coefficient v2 of prompt Ds+ mesons was measured in Pb–Pb collisions in the 30–50% centrality interval and is found to be compatible with that of non-strange D mesons. The main features of the measured RAA, Ds+/D0 ratio, and v2 as a function of pT are described by theoretical calculations of charm-quark transport in a hydrodynamically expanding quark–gluon plasma including hadronisation via charm-quark recombination with light quarks from the medium. The pT-integrated production yield of Ds+ mesons is compatible with the prediction of the statistical hadronisation model