Knowledge of left atrial (LA) anatomy is important for atrial fibrillation ablation guidance, fibrosis quantification and biophysical modelling. Segmentation of the LA from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) images is a complex problem. This manuscript presents a benchmark to evaluate algorithms that address LA segmentation. The datasets, ground truth and evaluation code have been made publicly available through the http://www.cardiacatlas.org website. This manuscript also reports the results of the Left Atrial Segmentation Challenge (LASC) carried out at the STACOM'13 workshop, in conjunction with MICCAI'13. Thirty CT and 30 MRI datasets were provided to participants for segmentation. Each participant segmented the LA including a short part of the LA appendage trunk and proximal sections of the pulmonary veins (PVs). We present results for nine algorithms for CT and eight algorithms for MRI. Results showed that methodologies combining statistical models with region growing approaches were the most appropriate to handle the proposed task. The ground truth and automatic segmentations were standardised to reduce the influence of inconsistently defined regions (e.g., mitral plane, PVs end points, LA appendage). This standardisation framework, which is a contribution of this work, can be used to label and further analyse anatomical regions of the LA. By performing the standardisation directly on the left atrial surface, we can process multiple input data, including meshes exported from different electroanatomical mapping systems.

Intestinal organoids are multicellular crypt-like structures that can be derived from adult intestinal stem cells (ISCs), embryonic stem cells (ESCs) or induced pluripotent stem cells (IPSCs). Here we show that intestinal organoids generated from mouse ESCs were enriched in ISCs and early progenitors. Treatment of these organoids with a γ-secretase inhibitor increased Math1 and decreased Hes1 expression, indicating Notch signaling regulates ISC differentiation in these organoids. Lgr5 and Tert positive ISCs constituted approximately 10% and 20% of the organoids. As found in native tissue, Lgr5 and Tert expressing cells resolved into two discreet populations, which were stable over time. Intestinal organoids derived from cancer-prone Apc(Min/+) mice showed similar numbers of ISCs, but had reduced Math1 expression, indicating a suppressed secretory cell differentiation potential (as found in intestinal tissue). Apc(Min/+) organoids were used to screen epigenetically active compounds for those that increased Math1 expression and organoid differentiation (including HDAC inhibitors, Sirtuin (SIRT) modulators and methyltransferase inhibitors). Broad-spectrum HDAC inhibitors increased both Math1 and Muc2 expression, indicating an ability to promote the suppressed secretory cell differentiation pathway. Other epigenetic compounds had a diverse impact on cell differentiation, with a strong negative correlation between those that activated the secretory marker Muc2 and those that activated the absorptive cell marker Fabp2. These data show that ESC-derived intestinal organoids can be derived in large numbers, contain distinct ISC types and can be used to screen for agents that promote cell differentiation through different lineage pathways.

Our results suggest a central role for cell-mediated cytotoxicity by the granule exocytosis pathway probably because of auto-cytotoxic T-cell clones in the pathogenesis of lichen planus.

Most artificial intelligence (AI) research and innovations have concentrated in high-income countries, where imaging data, IT infrastructures and clinical expertise are plentiful. However, slower progress has been made in limited-resource environments where medical imaging is needed. For example, in Sub-Saharan Africa, the rate of perinatal mortality is very high due to limited access to antenatal screening. In these countries, AI models could be implemented to help clinicians acquire fetal ultrasound planes for the diagnosis of fetal abnormalities. So far, deep learning models have been proposed to identify standard fetal planes, but there is no evidence of their ability to generalise in centres with low resources, i.e. with limited access to high-end ultrasound equipment and ultrasound data. This work investigates for the first time different strategies to reduce the domain-shift effect arising from a fetal plane classification model trained on one clinical centre with high-resource settings and transferred to a new centre with low-resource settings. To that end, a classifier trained with 1792 patients from Spain is first evaluated on a new centre in Denmark in optimal conditions with 1008 patients and is later optimised to reach the same performance in five African centres (Egypt, Algeria, Uganda, Ghana and Malawi) with 25 patients each. The results show that a transfer learning approach for domain adaptation can be a solution to integrate small-size African samples with existing large-scale databases in developed countries. In particular, the model can be re-aligned and optimised to boost the performance on African populations by increasing the recall to $$0.92 \pm 0.04$$ 0.92 ± 0.04 and at the same time maintaining a high precision across centres. This framework shows promise for building new AI models generalisable across clinical centres with limited data acquired in challenging and heterogeneous conditions and calls for further research to develop new solutions for the usability of AI in countries with fewer resources and, consequently, in higher need of clinical support.

Segmentation of the left ventricle in MRI images is a task with important diagnostic power. Currently, the evaluation of cardiac function involves the global measurement of volumes and ejection fraction. This evaluation requires the segmentation of the left ventricle contour. In this paper, we propose a new method for automatic detection of the endocardial border in cardiac magnetic resonance images, by using a level set segmentation-based approach. To initialize this level set segmentation algorithm, we propose to threshold the original image and to use the binary image obtained as initial mask for the level set segmentation method. For the localization of the left ventricular cavity, used to pose the initial binary mask, we propose an automatic approach to detect this spatial position by the evaluation of a metric indicating object's roundness. The segmentation process starts by the initialization of the level set algorithm and ended up through a level set segmentation. The validation process is achieved by comparing the segmentation results, obtained by the automated proposed segmentation process, to manual contours traced by tow experts. The database used was containing one automated and two manual segmentations for each sequence of images. This comparison showed good results with an overall average similarity area of 97.89%.