Staining Pattern Classification of Antinuclear Autoantibodies Based on Block Segmentation in Indirect Immunofluorescence Images

<div><p>Indirect immunofluorescence based on HEp-2 cell substrate is the most commonly used staining method for antinuclear autoantibodies associated with different types of autoimmune pathologies. The aim of this paper is to design an automatic system to identify the staining patterns based on block segmentation compared to the cell segmentation most used in previous research. Various feature descriptors and classifiers are tested and compared in the classification of the staining pattern of blocks and it is found that the technique of the combination of the local binary pattern and the k-nearest neighbor algorithm achieve the best performance. Relying on the results of block pattern classification, experiments on the whole images show that classifier fusion rules are able to identify the staining patterns of the whole well (specimen image) with a total accuracy of about 94.62%.</p></div

Method flow in the classification architecture.

<p>Method flow in the classification architecture.</p

ANA patterns in the experimental dataset: (a) coarse speckled (b) fine speckled (c) nucleolar (d) peripheral.

<p>ANA patterns in the experimental dataset: (a) coarse speckled (b) fine speckled (c) nucleolar (d) peripheral.</p

Architecture of well pattern classification.

<p>Architecture of well pattern classification.</p

Fluorous Liquids for Magnetic Resonance-Based Thermometry with Enhanced Responsiveness and Environmental Degradation

Accurate temperature measurement via magnetic resonance is valuable for both in vitro and in vivo analysis of local tissue for evaluating disease pathology and medical interventions. 1H MRI-based thermometry is used clinically but is susceptible to error from magnetic field drift and low sensitivity in fatty tissue and requires a reference for absolute temperature determination. As an alternative, perfluorotributylamine (PFTBA), a perfluorocarbon liquid for 19F MRI thermometry, is based on chemical shift responsiveness and approaches the sensitivity of 1H MRI thermometry agents; however, environmental persistence, greenhouse gas concerns, and multiple resonances which can lead to MRI artifacts indicate a need for alternative sensors. Using a 19F NMR-based structure–property study of synthetic organofluorine molecules, this research develops new organofluorine liquids with improved temperature responsiveness, high signal, and reduced nonmagnetically equivalent fluorine resonances. Environmental degradation analysis using reverse-phase HPLC and quantitative 19F NMR demonstrates a rapid degradation profile mediated via the aryl fluorine core of temperature sensors. Our findings show that our lead liquid temperature sensor, DD-1, can be made in high yield in a single step and possesses an improved responsiveness over our prior work and an 83% increase in aqueous thermal responsiveness over PFTBA. Degradation studies indicate robust degradation with half-lives of less than two hours under photolysis conditions for the parent compound and formation of other fluorinated products. The improved performance of DD-1 and its susceptibility to environmental degradation highlight a new lead fluorous liquid for thermometry applications

ANA pattern classification methods: segmentation, feature extraction and classification.

<p>ANA pattern classification methods: segmentation, feature extraction and classification.</p

Classification results based on the whole image.

<p>Classification results based on the whole image.</p

Overlap in the block segmentation: (a) calculate the centre of the connected regions; (b) overlap problem; (c) the overlapping rates with the size increasing.

<p>Overlap in the block segmentation: (a) calculate the centre of the connected regions; (b) overlap problem; (c) the overlapping rates with the size increasing.</p

Four staining patterns and the corresponding descriptors: (a) coarse speckled (CS); (b) fine speckled (FS); (c) nucleolar (NU); (d) peripheral (PE).

<p>Four staining patterns and the corresponding descriptors: (a) coarse speckled (CS); (b) fine speckled (FS); (c) nucleolar (NU); (d) peripheral (PE).</p

Comparison of direct whole image classification, classification based on cell segmentation and classification based on block segmentation.

<p>Comparison of direct whole image classification, classification based on cell segmentation and classification based on block segmentation.</p