14,014 research outputs found
A flexible geometric model for leaf shape descriptions with high accuracy
Accurate assessment of canopy structure is crucial in studying plant-environment interactions. The advancement of functional-structural plant models (FSPM), which incorporate the 3D structure of individual plants, increases the need for a method for accurate mathematical descriptions of leaf shape. A model was developed as an improvement of an existing leaf shape algorithm to describe a large variety of leaf shapes. Modelling accuracy was evaluated using a spatial segmentation method and shape differences were assessed using principal component analysis (PCA) on the optimised parameters. Furthermore, a method is presented to calculate the mean shape of a dataset, intended for obtaining a representative shape for modelling purposes. The presented model is able to accurately capture a large range of single, entire leaf shapes. PCA illustrated the interpretability of the parameter values and allowed evaluation of shape differences. The model parameters allow straightforward digital reconstruction of leaf shapes for modelling purposes such as FSPMs
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers. However, mitosis counting is laborious, subjective and may suffer from low inter-observer agreement. With the wider acceptance of whole slide images in pathology labs, automatic image analysis has been proposed as a potential solution for these issues.
In this paper, the results from the Assessment of Mitosis Detection Algorithms 2013 (AMIDA13) challenge are described. The challenge was based on a data set consisting of 12 training and 11 testing subjects, with more than one thousand annotated mitotic figures by multiple observers. Short descriptions and results from the evaluation of eleven methods are presented. The top performing method has an error rate that is comparable to the inter-observer agreement among pathologists
Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images
Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images
of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL
maps are derived through computational staining using a convolutional neural network trained to
classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and
correlation with overall survival. TIL map structural patterns were grouped using standard
histopathological parameters. These patterns are enriched in particular T cell subpopulations
derived from molecular measures. TIL densities and spatial structure were differentially enriched
among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial
infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic
patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for
the TCGA image archives with insights into the tumor-immune microenvironment
An investigation into inter- and intragenomic variations of graphic genomic signatures
We provide, on an extensive dataset and using several different distances,
confirmation of the hypothesis that CGR patterns are preserved along a genomic
DNA sequence, and are different for DNA sequences originating from genomes of
different species. This finding lends support to the theory that CGRs of
genomic sequences can act as graphic genomic signatures. In particular, we
compare the CGR patterns of over five hundred different 150,000 bp genomic
sequences originating from the genomes of six organisms, each belonging to one
of the kingdoms of life: H. sapiens, S. cerevisiae, A. thaliana, P. falciparum,
E. coli, and P. furiosus. We also provide preliminary evidence of this method's
applicability to closely related species by comparing H. sapiens (chromosome
21) sequences and over one hundred and fifty genomic sequences, also 150,000 bp
long, from P. troglodytes (Animalia; chromosome Y), for a total length of more
than 101 million basepairs analyzed. We compute pairwise distances between CGRs
of these genomic sequences using six different distances, and construct
Molecular Distance Maps that visualize all sequences as points in a
two-dimensional or three-dimensional space, to simultaneously display their
interrelationships. Our analysis confirms that CGR patterns of DNA sequences
from the same genome are in general quantitatively similar, while being
different for DNA sequences from genomes of different species. Our analysis of
the performance of the assessed distances uses three different quality measures
and suggests that several distances outperform the Euclidean distance, which
has so far been almost exclusively used for such studies. In particular we show
that, for this dataset, DSSIM (Structural Dissimilarity Index) and the
descriptor distance (introduced here) are best able to classify genomic
sequences.Comment: 14 pages, 6 figures, 5 table
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