1,666 research outputs found
Bringing Open Data to Whole Slide Imaging
Supplementary information associated with Besson et al. (2019) ECDP 2019
Faced with the need to support a growing number of whole slide imaging (WSI) file formats, our team has extended a long-standing community file format (OME-TIFF) for use in digital pathology. The format makes use of the core TIFF specification to store multi-resolution (or "pyramidal") representations of a single slide in a flexible, performant manner. Here we describe the structure of this format, its performance characteristics, as well as an open-source library support for reading and writing pyramidal OME-TIFFs
Digital pathology in clinical use: where are we now and what is holding us back?
Whole slide imaging is being used increasingly in research applications and in frozen section, consultation and external quality assurance practice. Digital pathology, when integrated with other digital tools such as barcoding, specimen tracking and digital dictation, can be integrated into the histopathology workflow, from specimen accession to report sign-out. These elements can bring about improvements in the safety, quality and efficiency of a histopathology department. The present paper reviews the evidence for these benefits. We then discuss the challenges of implementing a fully digital pathology workflow, including the regulatory environment, validation of whole slide imaging and the evidence for the design of a digital pathology workstation
Characterization of Posidonia Oceanica Seagrass Aerenchyma through Whole Slide Imaging: A Pilot Study
Characterizing the tissue morphology and anatomy of seagrasses is essential
to predicting their acoustic behavior. In this pilot study, we use histology
techniques and whole slide imaging (WSI) to describe the composition and
topology of the aerenchyma of an entire leaf blade in an automatic way
combining the advantages of X-ray microtomography and optical microscopy.
Paraffin blocks are prepared in such a way that microtome slices contain an
arbitrarily large number of cross sections distributed along the full length of
a blade. The sample organization in the paraffin block coupled with whole slide
image analysis allows high throughput data extraction and an exhaustive
characterization along the whole blade length. The core of the work are image
processing algorithms that can identify cells and air lacunae (or void) from
fiber strand, epidermis, mesophyll and vascular system. A set of specific
features is developed to adequately describe the convexity of cells and voids
where standard descriptors fail. The features scrutinize the local curvature of
the object borders to allow an accurate discrimination between void and cell
through machine learning. The algorithm allows to reconstruct the cells and
cell membrane features that are relevant to tissue density, compressibility and
rigidity. Size distribution of the different cell types and gas spaces, total
biomass and total void volume fraction are then extracted from the high
resolution slices to provide a complete characterization of the tissue along
the leave from its base to the apex
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