A Hybrid Transformer-Convolutional Neural Network for Segmentation of Intracerebral Hemorrhage and Perihematomal Edema on Non-Contrast Head Computed Tomography (CT) with Uncertainty Quantification to Improve Confidence.

Intracerebral hemorrhage (ICH) and perihematomal edema (PHE) are key imaging markers of primary and secondary brain injury in hemorrhagic stroke. Accurate segmentation and quantification of ICH and PHE can help with prognostication and guide treatment planning. In this study, we combined Swin-Unet Transformers with nnU-NETv2 convolutional network for segmentation of ICH and PHE on non-contrast head CTs.

Style mixup enhanced disentanglement learning for unsupervised domain adaptation in medical image segmentation.

Unsupervised domain adaptation (UDA) has shown impressive performance by improving the generalizability of the model to tackle the domain shift problem for cross-modality medical segmentation. However, most of the existing UDA approaches depend on high-quality image translation with diversity constraints to explicitly augment the potential data diversity, which is hard to ensure semantic consistency and capture domain-invariant representation. In this paper, free of image translation and diversity constraints, we propose a novel Style Mixup Enhanced Disentanglement Learning (SMEDL) for UDA medical image segmentation to further improve domain generalization and enhance domain-invariant learning ability.

A cross-sectional survey of farmer reported prevalence and farm management practices associated with neonatal infectious arthritis ("joint ill") in lambs, on UK sheep farms.

Introduction: Neonatal infectious arthritis (NIA) is a bacterial disease of lambs in the first month of life. NIA is associated with poor animal welfare, economic losses, and prophylactic antibiotic use. Farmers report problems with NIA despite following current guidance on prevention.

Progression of the faecal microbiome in preweaning dairy calves that develop cryptosporidiosis.

Background: Cryptosporidiosis is a diarrheal disease that commonly affects calves under 6 weeks old. The causative agent, Cryptosporidium parvum, has been associated with the abundance of specific taxa in the faecal microbiome during active infection. However, the long-term impact of these microbiome shifts, and potential effects on calf growth and health have not yet been explored in depth.