106 research outputs found
Deep Active Learning for Automatic Mitotic Cell Detection on HEp-2 Specimen Medical Images
Identifying Human Epithelial Type 2 (HEp-2) mitotic cells is a crucial procedure in anti-nuclear antibodies (ANAs) testing, which is the standard protocol for detecting connective tissue diseases (CTD). Due to the low throughput and labor-subjectivity of the ANAs' manual screening test, there is a need to develop a reliable HEp-2 computer-aided diagnosis (CAD) system. The automatic detection of mitotic cells from the microscopic HEp-2 specimen images is an essential step to support the diagnosis process and enhance the throughput of this test. This work proposes a deep active learning (DAL) approach to overcoming the cell labeling challenge. Moreover, deep learning detectors are tailored to automatically identify the mitotic cells directly in the entire microscopic HEp-2 specimen images, avoiding the segmentation step. The proposed framework is validated using the I3A Task-2 dataset over 5-fold cross-validation trials. Using the YOLO predictor, promising mitotic cell prediction results are achieved with an average of 90.011% recall, 88.307% precision, and 81.531% mAP. Whereas, average scores of 86.986% recall, 85.282% precision, and 78.506% mAP are obtained using the Faster R-CNN predictor. Employing the DAL method over four labeling rounds effectively enhances the accuracy of the data annotation, and hence, improves the prediction performance. The proposed framework could be practically applicable to support medical personnel in making rapid and accurate decisions about the mitotic cells' existence
Modelling Neuron Morphology: Automated Reconstruction from Microscopy Images
Understanding how the brain works is, beyond a shadow of doubt, one of the greatest challenges for modern science. Achieving a deep knowledge about the structure, function and development of the nervous system at the molecular, cellular and network levels is crucial in this attempt, as processes at all these scales are intrinsically linked with higher-order cognitive functions. The research in the various areas of neuroscience deals with advanced imaging techniques, collecting an increasing amounts of heterogeneous and complex data at different scales. Then, computational tools and neuroinformatics solutions are required in order to integrate and analyze the massive quantity of acquired information.
Within this context, the development of automaticmethods and tools for the study of neuronal anatomy has a central role. The morphological properties of the soma and of the axonal and dendritic arborizations constitute a key discriminant for the neuronal phenotype and play a determinant role in network connectivity. A quantitative analysis allows the study of possible factors influencing neuronal development, the neuropathological abnormalities related to specific syndromes, the relationships
between neuronal shape and function, the signal transmission and the network connectivity. Therefore, three-dimensional digital reconstructions of soma, axons and dendrites are indispensable for exploring neural networks. This thesis proposes a novel and completely automatic pipeline for neuron reconstruction with operations ranging from the detection and segmentation of the soma to the dendritic arborization tracing. The pipeline can deal with different datasets and acquisitions both at the network and at the single scale level without any user interventions or manual adjustment. We developed an ad hoc approach for the localization and segmentation of neuron bodies. Then, various methods and research lines have been investigated for the reconstruction of the whole dendritic arborization of each neuron, which is solved both in 2D and in 3D images
SEGMENTATION AND INFORMATICS IN MULTIDIMENSIONAL FLUORESCENCE OPTICAL MICROSCOPY IMAGES
Recent advances in the field of optical microscopy have enabled scientists to
observe and image complex biological processes across a wide range of spatial and
temporal resolution, resulting in an exponential increase in optical microscopy data.
Manual analysis of such large volumes of data is extremely time consuming and often
impossible if the changes cannot be detected by the human eye. Naturally it is essential
to design robust, accurate and high performance image processing and analysis
tools to extract biologically significant results. Furthermore, the presentation of the
results to the end-user, post analysis, is also an equally challenging issue, especially
when the data (and/or the hypothesis) involves several spatial/hierarchical scales
(e.g., tissues, cells, (sub)-nuclear components). This dissertation concentrates on
a subset of such problems such as robust edge detection, automatic nuclear segmentation
and selection in multi-dimensional tissue images, spatial analysis of gene
localization within the cell nucleus, information visualization and the development
of a computational framework for efficient and high-throughput processing of large
datasets.
Initially, we have developed 2D nuclear segmentation and selection algorithms
which help in the development of an integrated approach for determining the preferential
spatial localization of certain genes within the cell nuclei which is emerging
as a promising technique for the diagnosis of breast cancer. Quantification requires
accurate segmentation of 100 to 200 cell nuclei in each patient tissue sample in order
to draw a statistically significant result. Thus, for large scale analysis involving hundreds
of patients, manual processing is too time consuming and subjective. We have
developed an integrated workflow that selects, following 2D automatic segmentation,
a sub-population of accurately delineated nuclei for positioning of fluorescence in
situ hybridization labeled genes of interest in tissue samples. Application of the
method was demonstrated for discriminating normal and cancerous breast tissue
sections based on the differential positioning of the HES5 gene. Automatic results
agreed with manual analysis in 11 out of 14 cancers, all 4 normal cases and all 5
non-cancerous breast disease cases, thus showing the accuracy and robustness of the
proposed approach.
As a natural progression from the 2D analysis algorithms to 3D, we first developed
a robust and accurate probabilistic edge detection method for 3D tissue
samples since several down stream analysis procedures such as segmentation and
tracking rely on the performance of edge detection. The method based on multiscale
and multi-orientation steps surpasses several other conventional edge detectors
in terms of its performance. Subsequently, given an appropriate edge measure, we
developed an optimal graphcut-based 3D nuclear segmentation technique for samples
where the cell nuclei are volume or surface labeled. It poses the problem as
one of finding minimal closure in a directed graph and solves it efficiently using the
maxflow-mincut algorithm. Both interactive and automatic versions of the algorithm
are developed. The algorithm outperforms, in terms of three metrics that are
commonly used to evaluate segmentation algorithms, a recently reported geodesic
distance transform-based 3D nuclear segmentation method which in turns was reported
to outperform several other popular tools that segment 3D nuclei in tissue
samples.
Finally, to apply some of the aforementioned methods to large microscopic
datasets, we have developed a user friendly computing environment called MiPipeline
which supports high throughput data analysis, data and process provenance,
visual programming and seamlessly integrated information visualization of hierarchical
biological data. The computational part of the environment is based on LONI
Pipeline distributed computing server and the interactive information visualization
makes use of several javascript based libraries to visualize an XML-based backbone
file populated with essential meta-data and results
Deep Learning based HEp-2 Image Classification: A Comprehensive Review
Classification of HEp-2 cell patterns plays a significant role in the
indirect immunofluorescence test for identifying autoimmune diseases in the
human body. Many automatic HEp-2 cell classification methods have been proposed
in recent years, amongst which deep learning based methods have shown
impressive performance. This paper provides a comprehensive review of the
existing deep learning based HEp-2 cell image classification methods. These
methods perform HEp-2 image classification at two levels, namely, cell-level
and specimen-level. Both levels are covered in this review. At each level, the
methods are organized with a deep network usage based taxonomy. The core idea,
notable achievements, and key strengths and weaknesses of each method are
critically analyzed. Furthermore, a concise review of the existing HEp-2
datasets that are commonly used in the literature is given. The paper ends with
a discussion on novel opportunities and future research directions in this
field. It is hoped that this paper would provide readers with a thorough
reference of this novel, challenging, and thriving field.Comment: Published in Medical Image Analysi
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Validation of super-resolution 4Pi-microscopy, and its application to the study of intra-Golgi transport
Two problems have hampered the use of light microscopy for structural studies of cellular organelles for a long time: the limited spatial resolution, and the difficulty of obtaining true structural boundaries from complex intensity curves. The advent of modern super-resolution light-microscopy techniques and their combination with objective image segmentation now provide us with the means to bridge the gap between light and electron microscopy in cell biological applications. This study provides the first comparative correlative analysis of three-dimensional (3D) structures obtained by 4Pi-microscopy with those of transmission microscopy (TEM). The distribution within the cisternae of isolated Golgi stacks of the cargo protein PC3-pEGFP is here mapped in 3D by both 4Pi-microscopy and TEM for a detailed comparative analysis of their imaging capabilities. A high correlation was seen for these structures, demonstrating the particular accuracy of 4Pi-microscopy. Furthermore, for the first time, the transport of a cargo molecule (VSVG-pEGFP) through individual Golgi stacks (labelled by GalT-venusYFP) is visualized at the 4Pi-microscope super-resolution level. Following the procedures validated by the correlative analysis, the transport experiments show that: 1) VSVG-pEGFP rapidly enter/exit individual Golgi stack; 2) VSVG-pEGFP never fills the GalT-venusYFP compartment completely; and 3) the GalT-venusYFP compartment volume increases upon VSVG-pEGFP arrival. This morphological evidence supports some previous TEM-based observations of the intra-Golgi transport of VSVG-pEGFP and provides new insights towards a better understanding of protein transport across the Golgi stack. This study thus demonstrates the general applicability of super-resolution 4Pi-microscopy coupled with the zero-crossing-based segmentation technique for structural studies of sub-organelle protein distributions in living cells
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