Semi-automated learning strategies for large-scale segmentation of histology and other big bioimaging stacks and volumes

Labelled high-resolution datasets are becoming increasingly common and necessary in different areas of biomedical imaging. Examples include: serial histology and ex-vivo MRI for atlas building, OCT for studying the human brain, and micro X-ray for tissue engineering. Labelling such datasets, typically, requires manual delineation of a very detailed set of regions of interest on a large number of sections or slices. This process is tedious, time-consuming, not reproducible and rather inefficient due to the high similarity of adjacent sections. In this thesis, I explore the potential of a semi-automated slice level segmentation framework and a suggestive region level framework which aim to speed up the segmentation process of big bioimaging datasets. The thesis includes two well validated, published, and widely used novel methods and one algorithm which did not yield an improvement compared to the current state-of the-art. The slice-wise method, SmartInterpol, consists of a probabilistic model for semi-automated segmentation of stacks of 2D images, in which the user manually labels a sparse set of sections (e.g., one every n sections), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation and convolutional neural networks. Labelling every structure on a sparse set of slices is not necessarily optimal, therefore I also introduce a region level active learning framework which requires the labeller to annotate one region of interest on one slice at the time. The framework exploits partial annotations, weak supervision, and realistic estimates of class and section-specific annotation effort in order to greatly reduce the time it takes to produce accurate segmentations for large histological datasets. Although both frameworks have been created targeting histological datasets, they have been successfully applied to other big bioimaging datasets, reducing labelling effort by up to 60−70% without compromising accuracy

Advanced x-ray imaging techniques in tissue engineering: a new construct assessment platform for enabling the regeneration of personalised organs

Tissue engineering (TE) holds promise for generating lab-grown patient specific organs which can provide: (1) effective treatment for conditions that require volumetric tissue transplantation and (2) new platforms for drug testing. Even though volumetric structural information is essential for confirming successful organ maturation, TE protocol designs are currently informed through destructive and 2D construct assessment tools (e.g. histology). X-ray phase-contrast computed-tomography (PC-CT) can generate non-destructive, high resolution, 3D density maps of organ architecture. In this work, PC-CT is used as new imaging tool for guiding two TE protocols currently at the in-vitro testing stage. The first (1) involves cell-repopulation of an oesophageal scaffold, with the aim of using the regenerated construct for treating long-gap oesophageal atresia, whilst for the second (2) a lung-derived scaffold is populated with islets for regenerating a pancreas, with the “repurposed” lung offering a platform for diabetes drug testing. By combing 3D images and quantitative information, we were able to perform comprehensive construct evaluation. Specifically, we assessed volumetrically: (1) the cell-distribution within the regenerated oesophagi and (2) islet integration with the vascular tree of the lung-derived scaffold. This new information was proven to be essential for establishing corresponding TE protocols and enabled their progression to more advanced scale-up models. We are confident that PC-CT will provide the novel insights necessary to further progress TE protocols, with the next step being in-vivo testing. Crucially, the non-destructive nature of PC-CT will allow in-vivo assessments of TE constructs following their implantation into animal hosts, to investigate their successful integration

First breeding of Audouin’s Gull, Larus audouinii, in the Parco Naturale Regionale Molentargius - Saline (Sardinia)

In 2007 a colony of Audouin’s Gull settled for the first time in Molentargius saltpans, which represent a non-typical habitat for this (formerly) strictly marine species. The 64 breeding pairs had a productivity of ca. 0.2 chicks/pair. Within Sardinia, this is the third breeding site located on coastal wetlands/saltpans. These habitats host today more than 40% of the Italian population, playing therefore a key role in the conservation of this endangered species

Perivascular space dilation is associated with vascular amyloid-β accumulation in the overlying cortex

Abstract Perivascular spaces (PVS) are compartments surrounding cerebral blood vessels that become visible on MRI when enlarged. Enlarged PVS (EPVS) are commonly seen in patients with cerebral small vessel disease (CSVD) and have been suggested to reflect dysfunctional perivascular clearance of soluble waste products from the brain. In this study, we investigated histopathological correlates of EPVS and how they relate to vascular amyloid-β (Aβ) in cerebral amyloid angiopathy (CAA), a form of CSVD that commonly co-exists with Alzheimer’s disease (AD) pathology. We used ex vivo MRI, semi-automatic segmentation and validated deep-learning-based models to quantify EPVS and associated histopathological abnormalities. Severity of MRI-visible PVS during life was significantly associated with severity of MRI-visible PVS on ex vivo MRI in formalin fixed intact hemispheres and corresponded with PVS enlargement on histopathology in the same areas. EPVS were located mainly around the white matter portion of perforating cortical arterioles and their burden was associated with CAA severity in the overlying cortex. Furthermore, we observed markedly reduced smooth muscle cells and increased vascular Aβ accumulation, extending into the WM, in individually affected vessels with an EPVS. Overall, these findings are consistent with the notion that EPVS reflect impaired outward flow along arterioles and have implications for our understanding of perivascular clearance mechanisms, which play an important role in the pathophysiology of CAA and AD

Registered histology, MRI, and manual annotations of over 300 brain regions in 5 human hemispheres (data from ERC Starting Grant 677697 "BUNGEE-TOOLS")

Summary: This repository includes data related to the ERC Starting Grant project 677697: "Building Next-Generation Computational Tools for High Resolution Neuroimaging Studies" (BUNGEE-TOOLS). It includes: (a) Dense histological sections from five human hemispheres with manual delineations of >300 brain regions; (b) Corresponding ex vivo MRI scans; (c) Dissection photographs; (d) A spatially aligned version of the dataset; (e) A probabilistic atlas built from the hemispheres; and (f) Code to apply the atlas to automated segmentation of in vivo MRI scans. More detailed description on what this dataset includes: Data files and Python code for Bayesian segmentation of human brain MRI based on a next-generation, high-resolution histological atlas: "Next-Generation histological atlas for high-resolution segmentation of human brain MRI" A Casamitjana et al., in preparation.  This repository contains a set of zip files, each corresponding to one directory. Once decompressed, each directory has a readme.txt file explaining its contents.   The list of zip files / compressed directories is:   - 3dAtlas.zip: nifti files with summary imaging volumes of the probabilistic atlas.   - BlockFacePhotoBlocks.zip: nifti files with the blackface photographs acquired during   tissue sectioning, reconstructed into 3D volumes (in RGB).    - Histology.zip: jpg files with the LFB and H&E stained sections.   - HistologySegmentations.zip: 2D nifti files with the segmentations of the histological sections.   - MRI.zip: ex vivo T2-weighted MRI scans and corresponding FreeSurfer processing files   - SegmentationCode.zip: contains the the Python code and data files that we used to segment   brain MRI scans and obtain the results presented in the article (for reproducibility purposes).   Note that it requires an installation of FreeSurfer. Also, note that the code is also maintained    in FreeSurfer (but may not produce exactly the same results):   https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation   - WholeHemispherePhotos.zip: photographs of the specimens prior to dissection   - WholeSlicePhotos.zip: photographs of the tissue slabs prior to blocking.    We also note that the registered images for the five cases can be found in GitHub:  https://github.com/UCL/BrainAtlas-P41-16  https://github.com/UCL/BrainAtlas-P57-16  https://github.com/UCL/BrainAtlas-P58-16  https://github.com/UCL/BrainAtlas-P85-18  https://github.com/UCL/BrainAtlas-EX9-19   These registered images can be interactively explored with the following web interface:  https://github-pages.ucl.ac.uk/BrainAtlas/#/atlas</p

Long-term safety of COVID vaccination in individuals with idiopathic inflammatory myopathies: results from the COVAD study

Limited evidence on long-term COVID-19 vaccine safety in patients with idiopathic inflammatory myopathies (IIMs) continues to contribute to vaccine hesitancy. We studied delayed-onset vaccine adverse events (AEs) in patients with IIMs, other systemic autoimmune and inflammatory disorders (SAIDs), and healthy controls (HCs), using data from the second COVID-19 Vaccination in Autoimmune Diseases (COVAD) study. A validated self-reporting e-survey was circulated by the COVAD study group (157 collaborators, 106 countries) from Feb-June 2022. We collected data on demographics, comorbidities, IIM/SAID details, COVID-19 history, and vaccination details. Delayed-onset (> 7 day) AEs were analyzed using regression models. A total of 15165 respondents undertook the survey, of whom 8759 responses from vaccinated individuals [median age 46 (35-58) years, 74.4% females, 45.4% Caucasians] were analyzed. Of these, 1390 (15.9%) had IIMs, 50.6% other SAIDs, and 33.5% HCs. Among IIMs, 16.3% and 10.2% patients reported minor and major AEs, respectively, and 0.72% (n = 10) required hospitalization. Notably patients with IIMs experienced fewer minor AEs than other SAIDs, though rashes were expectedly more than HCs [OR 4.0; 95% CI 2.2-7.0, p < 0.001]. IIM patients with active disease, overlap myositis, autoimmune comorbidities, and ChadOx1 nCOV-19 (Oxford/AstraZeneca) recipients reported AEs more often, while those with inclusion body myositis, and BNT162b2 (Pfizer) recipients reported fewer AEs. Vaccination is reassuringly safe in individuals with IIMs, with AEs, hospitalizations comparable to SAIDs, and largely limited to those with autoimmune multimorbidity and active disease. These observations may inform guidelines to identify high-risk patients warranting close monitoring in the post-vaccination period