1,575 research outputs found
SuRVoS: Super-Region Volume Segmentation workbench
Segmentation of biological volumes is a crucial step needed to fully analyse their scientific content. Not having access to convenient tools with which to segment or annotate the data means many biological volumes remain under-utilised. Automatic segmentation of biological volumes is still a very challenging research field, and current methods usually require a large amount of manually-produced training data to deliver a high-quality segmentation. However, the complex appearance of cellular features and the high variance from one sample to another, along with the time-consuming work of manually labelling complete volumes, makes the required training data very scarce or non-existent. Thus, fully automatic approaches are often infeasible for many practical applications. With the aim of unifying the segmentation power of automatic approaches with the user expertise and ability to manually annotate biological samples, we present a new workbench named SuRVoS (Super-Region Volume Segmentation). Within this software, a volume to be segmented is first partitioned into hierarchical segmentation layers (named Super-Regions) and is then interactively segmented with the user's knowledge input in the form of training annotations. SuRVoS first learns from and then extends user inputs to the rest of the volume, while using Super-Regions for quicker and easier segmentation than when using a voxel grid. These benefits are especially noticeable on noisy, low-dose, biological datasets
The cell signaling structure function
Live cell microscopy captures 5-D movies that display
patterns of cellular motion and signaling dynamics. We present here an approach
to finding spatiotemporal patterns of cell signaling dynamics in 5-D live cell
microscopy movies unique in requiring no a priori knowledge of expected pattern
dynamics, and no training data. The proposed cell signaling structure function
(SSF) is a Kolmogorov structure function that optimally measures cell signaling
state as nuclear intensity w.r.t. surrounding cytoplasm, a significant
improvement compared to the current state-of-the-art cytonuclear ratio. SSF
kymographs store at each spatiotemporal cell centroid the SSF value, or a
functional output such as velocity. Patterns of similarity are identified via
the metric normalized compression distance (NCD). The NCD is a reproducing
kernel for a Hilbert space that represents the input SSF kymographs as points
in a low dimensional embedding that optimally captures the pattern similarity
identified by the NCD throughout the space. The only parameter is the expected
cell radii (). A new formulation of the cluster structure function
optimally estimates how meaningful an embedding from the RKHS representation.
Results are presented quantifying the impact of ERK and AKT signaling between
different oncogenic mutations, and by the relation between ERK signaling and
cellular velocity patterns for movies of 2-D monolayers of human breast
epithelial (MCF10A) cells, 3-D MCF10A spheroids under optogenetic manipulation
of ERK, and human induced pluripotent stem cells
Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques
A new approach for the segmentation of gland units in histological images is proposed with the aim of contributing to the improvement of the prostate cancer diagnosis. Clustering methods on several
colour spaces are applied to each sample in order to generate a binary
mask of the different tissue components. From the mask of lumen candidates, the Locally Constrained Watershed Transform (LCWT) is applied
as a novel gland segmentation technique never before used in this type
of images. 500 random gland candidates, both benign and pathological,
are selected to evaluate the LCWT technique providing results of Dice
coefficient of 0.85. Several shape and textural descriptors in combination
with contextual features and a fractal analysis are applied, in a novel
way, on different colour spaces achieving a total of 297 features to discern between artefacts and true glands. The most relevant features are
then selected by an exhaustive statistical analysis in terms of independence between variables and dependence with the class. 3.200 artefacts,
3.195 benign glands and 3.000 pathological glands are obtained, from a
data set of 1468 images at 10x magnification. A careful strategy of data
partition is implemented to robustly address the classification problem
between artefacts and glands. Both linear and non-linear approaches are
considered using machine learning techniques based on Support Vector
Machines (SVM) and feedforward neural networks achieving values of
sensitivity, specificity and accuracy of 0.92, 0.97 and 0.95, respectivelyThis work has been funded by the Ministry of Economy, Industry and Competitiveness under the SICAP project (DPI2016-77869-C2-1-R). The work
of Adri´an Colomer has been supported by the Spanish FPI Grant BES-2014-067889.
We gratefully acknowledge the support of NVIDIA Corporation with the donation of
the Titan Xp GPU used for this researchGarcĂa-Pardo, JG.; Colomer, A.; Naranjo Ornedo, V.; PeĂąaranda, F.; Sales, MĂ. (2018). Identification of Individual Glandular Regions Using LCWT and Machine Learning Techniques. En Intelligent Data Engineering and Automated Learning â IDEAL 2018. Springer. 642-650. https://doi.org/10.1007/978-3-030-03493-1_67S642650Gleason, D.F.: Histologic grading and clinical staging of prostatic carcinoma. In: Urologic Pathology (1977)Naik, S., Doyle, S., Feldman, M., Tomaszewski, J., Madabhushi, A.: Gland segmentation and computerized gleason grading of prostate histology by integrating low-, high-level and domain specific information. In: MIAAB Workshop, pp. 1â8 (2007)Nguyen, K., Sabata, B., Jain, A.K.: Prostate cancer grading: gland segmentation and structural features. Pattern Recogn. Lett. 33(7), 951â961 (2012)Kwak, J.T., Hewitt, S.M.: Multiview boosting digital pathology analysis of prostate cancer. Comput. Methods Programs Biomed. 142, 91â99 (2017)Ren, J., Sadimin, E., Foran, D.J., Qi, X.: Computer aided analysis of prostate histopathology images to support a refined gleason grading system. In: SPIE Medical Imaging, International Society for Optics and Photonics, p. 101331V (2017)Soille, P.: Morphological Image Analysis: Principles and Applications. Springer, Berlin (2013)Nguyen, K., Sarkar, A., Jain, A.K.: Structure and context in prostatic gland segmentation and classification. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7510, pp. 115â123. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33415-3_15Beare, R.: A locally constrained watershed transform. IEEE Trans. Pattern Anal. Mach. Intell. 28(7), 1063â1074 (2006)Gertych, A., et al.: Machine learning approaches to analyze histological images of tissues from radical prostatectomies. Comput. Med. Imaging Graph. 46, 197â208 (2015)Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971â987 (2002)Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657â1663 (2010)Huang, P., Lee, C.: Automatic classification for pathological prostate images based on fractal analysis. IEEE Trans. Med. Imaging 28(7), 1037â1050 (2009)Ruifrok, A.C., Johnston, D.A., et al.: Quantification of histochemical staining by color deconvolution. Anal. Quant. Cytol. Histol. 23(4), 291â299 (2001
Histopathological image analysis : a review
Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe
Direct visualization of degradation microcompartments at the ER membrane
To promote the biochemical reactions of life, cells can compartmentalize molecular interaction partners together within separated non-membrane-bound regions. It is unknown whether this strategy is used to facilitate protein degradation at specific locations within the cell. Leveraging in situ cryo-electron tomography to image the native molecular landscape of the unicellular alga Chlamydomonas reinhardtii, we discovered that the cytosolic protein degradation machinery is concentrated within similar to 200-nm foci that contact specialized patches of endoplasmic reticulum (ER) membrane away from the ER-Golgi interface. These non-membrane-bound microcompartments exclude ribosomes and consist of a core of densely clustered 265 proteasomes surrounded by a loose cloud of Cdc48. Active proteasomes in the microcompartments directly engage with putative substrate at the ER membrane, a function canonically assigned to Cdc48. Live-cell fluorescence microscopy revealed that the proteasome clusters are dynamic, with frequent assembly and fusion events. We propose that the microcompartments perform ER-associated degradation, colocalizing the degradation machinery at specific ER hot spots to enable efficient protein quality control
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