scholarly journals Resolution-agnostic tissue segmentation in whole-slide histopathology images with convolutional neural networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e8242 ◽  
Author(s):  
Péter Bándi ◽  
Maschenka Balkenhol ◽  
Bram van Ginneken ◽  
Jeroen van der Laak ◽  
Geert Litjens
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Tissue Segmentation ◽  
Traditional Methods ◽  
Tissue Samples ◽  
Multiple Networks ◽  
Tissue Sections ◽  
Medical Centers ◽  
Whole Slide Images

Modern pathology diagnostics is being driven toward large scale digitization of microscopic tissue sections. A prerequisite for its safe implementation is the guarantee that all tissue present on a glass slide can also be found back in the digital image. Whole-slide scanners perform a tissue segmentation in a low resolution overview image to prevent inefficient high-resolution scanning of empty background areas. However, currently applied algorithms can fail in detecting all tissue regions. In this study, we developed convolutional neural networks to distinguish tissue from background. We collected 100 whole-slide images of 10 tissue samples—staining categories from five medical centers for development and testing. Additionally, eight more images of eight unfamiliar categories were collected for testing only. We compared our fully-convolutional neural networks to three traditional methods on a range of resolution levels using Dice score and sensitivity. We also tested whether a single neural network can perform equivalently to multiple networks, each specialized in a single resolution. Overall, our solutions outperformed the traditional methods on all the tested resolutions. The resolution-agnostic network achieved average Dice scores between 0.97 and 0.98 across the tested resolution levels, only 0.0069 less than the resolution-specific networks. Finally, its excellent generalization performance was demonstrated by achieving averages of 0.98 Dice score and 0.97 sensitivity on the eight unfamiliar images. A future study should test this network prospectively.

scholarly journals Decidual Vasculopathy Identification in Whole Slide Images Using Multiresolution Hierarchical Convolutional Neural Networks

2020 ◽  
Vol 190 (10) ◽  
pp. 2111-2122
Author(s):  
Daniel Clymer ◽  
Stefan Kostadinov ◽  
Janet Catov ◽  
Lauren Skvarca ◽  
Liron Pantanowitz ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Whole Slide Images

Brain Tissue Segmentation via Deep Convolutional Neural Networks

2021 ◽  
Author(s):  
Thulasi Bikku ◽  
Jayavarapu Karthik ◽  
Ganga Rama Koteswara Rao ◽  
K P N V Satya Sree ◽  
P V V S Srinivas ◽  
...  
Keyword(s):  
Neural Networks ◽  
Brain Tissue ◽  
Convolutional Neural Networks ◽  
Tissue Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Brain Tissue Segmentation

scholarly journals MS2CNN: predicting MS/MS spectrum based on protein sequence using deep convolutional neural networks

BMC Genomics ◽  
2019 ◽  
Vol 20 (S9) ◽  
Author(s):  
Yang-Ming Lin ◽  
Ching-Tai Chen ◽  
Jia-Ming Chang
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Large Scale ◽  
Learning Algorithm ◽  
Pearson Correlation ◽  
Tandem Mass ◽  
Spectral Library ◽  
Deep Convolutional Neural Networks ◽  
Library Search

Abstract Background Tandem mass spectrometry allows biologists to identify and quantify protein samples in the form of digested peptide sequences. When performing peptide identification, spectral library search is more sensitive than traditional database search but is limited to peptides that have been previously identified. An accurate tandem mass spectrum prediction tool is thus crucial in expanding the peptide space and increasing the coverage of spectral library search. Results We propose MS2CNN, a non-linear regression model based on deep convolutional neural networks, a deep learning algorithm. The features for our model are amino acid composition, predicted secondary structure, and physical-chemical features such as isoelectric point, aromaticity, helicity, hydrophobicity, and basicity. MS2CNN was trained with five-fold cross validation on a three-way data split on the large-scale human HCD MS2 dataset of Orbitrap LC-MS/MS downloaded from the National Institute of Standards and Technology. It was then evaluated on a publicly available independent test dataset of human HeLa cell lysate from LC-MS experiments. On average, our model shows better cosine similarity and Pearson correlation coefficient (0.690 and 0.632) than MS2PIP (0.647 and 0.601) and is comparable with pDeep (0.692 and 0.642). Notably, for the more complex MS2 spectra of 3+ peptides, MS2PIP is significantly better than both MS2PIP and pDeep. Conclusions We showed that MS2CNN outperforms MS2PIP for 2+ and 3+ peptides and pDeep for 3+ peptides. This implies that MS2CNN, the proposed convolutional neural network model, generates highly accurate MS2 spectra for LC-MS/MS experiments using Orbitrap machines, which can be of great help in protein and peptide identifications. The results suggest that incorporating more data for deep learning model may improve performance.

scholarly journals Liver tissue segmentation in multiphase CT scans using cascaded convolutional neural networks

2019 ◽  
Vol 14 (8) ◽  
pp. 1275-1284 ◽  
Author(s):  
Farid Ouhmich ◽  
Vincent Agnus ◽  
Vincent Noblet ◽  
Fabrice Heitz ◽  
Patrick Pessaux
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Liver Tissue ◽  
Ct Scans ◽  
Tissue Segmentation ◽  
Multiphase Ct
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