This repository contains the image dataset and the manual annotations used to develop the HEPASS algorithm for automated liver steatosis quantification:
- Salvi M., Molinaro M., Metovic J., Patrono D., Romagnoli R., Papotti M, and Molinari F., "Fully Automated Quantitative Assessment of Hepatic Steatosis in Liver Transplants", Computers in Biology and Medicine 2020 (DOI: 10.1016/j.compbiomed.2020.103836)
Background: The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists’ visual evaluations on liver histology specimens.
Method: The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis.
Results: The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods.
Conclusions: To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 seconds), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.
Mass Spectrometry Imaging datasets used as validation of the functionality of rMSIcleanup (https://github.com/gbaquer/rMSIcleanup). Acquired with silver-assisted LDI using MALDI TOF/TOF ultrafleXtreme. Referred to as Dataset 1-10 in the accompanying publication (https://doi.org/10.1101/2019.12.20.884957). Datasets 1 and 2: Mouse Pancreatic Tissue. Dataset 3: Mouse Kidney Tissue. Datasets 4-10: Mouse Brain Tissue.
The data set contains Hadamard matrices of order 28 in machine-readable form, convenient for use in programs.
Data taken from the site
This data set complements following ones
Ukhalov, Alexey; Nevskii, Mikhail (2018), “Functions for checking necessary conditions for maximality of 0/1-determinant and example”, Mendeley Data, v1 http://dx.doi.org/10.17632/sm3x4xrb42.1
Ukhalov, Alexey (2019), “Matrices having the largest known determinant in machine-readable form”, Mendeley Data, v1 http://dx.doi.org/10.17632/hzf94h43c5.1
Data is presented in three formats: Wolfram Mathematica Notebook, PDF, and Plain Text.
Contributors:Rebecca L Hansen, Maria Emilia Dueñas, Young Jin Lee
Mass Spectrometry Imaging dataset of B73 inbred root used in the validation of of rMSIcleanup (https://github.com/gbaquer/rMSIcleanup). Acquired with silver-assisted LDI using Thermo Finnigan™ MALDI-LTQ-Orbitrap Discovery. The datasets are referred to as Dataset 13 and Dataset 14 in the accompanying publication (https://doi.org/10.1101/2019.12.20.884957).
Contributors:Kerem Özkap, Ertan Peksen, Ismail Kaplanvural, Deniz Çaka
This data and code are associated with the article "3D Scanner Technology Implementation to Numerical Modeling of GPR" by the same authors. The 3D scanner data and Matlab code used in the article are provided with other necessary files. The Readme file comprises detailed descriptions of the data files and formats.
Please see the publication for more information about this data set.
Contributors:Javier Pastor-Galindo, Mattia Zago, Pantaleone Nespoli, Sergio Lopez, Alberto Huertas Celdrán, Manuel Gil Pérez, José A. Ruipérez-Valiente, Gregorio Martinez Perez, Felix Gomez Marmol
While social media has been proved as an exceptionally useful tool to interact with other people and massively and quickly spread helpful information, its great potential has been ill-intentionally leveraged as well to distort political elections and manipulate constituents. In the paper at hand, we analyzed the presence and behavior of social bots on Twitter in the context of the November 2019 Spanish general election. Throughout our study, we classified involved users as social bots or humans, and examined their interactions from a quantitative (i.e., amount of traffic generated and existing relations) and qualitative (i.e., user's political affinity and sentiment towards the most important parties) perspectives. Results demonstrated that a non-negligible amount of those bots actively participated in the election, supporting each of the five principal political parties.
The dataset at hand presents the data collected during the observation period (from October 4th, 2019 to November 11th, 2019). It includes both the anonymized tweets and the users' data.
Data have been exported in three formats to provide the maximum flexibility
- MongoDB Dump BSONs: To import these data, please refer to the official MongoDB documentation.
- JSON Exports: Both the users and the tweets collections have been exported as canonical JSON files.
- CSV Exports (only tweets): The tweet collection has been exported as plain CSV file with comma separators.
This resource includes two hyperspectral images and a multispectral image in the area of the Three Rivers of Headwater region of China. The images were produced by Chinese environmental satellite HJ-1A, which is equipped with a CCD camera and a hyperspectral imager (HSI) on August 3, 2017. These remote sensing images was downloaded from the China center for resources satellite data and application for free.(website: http://www.cresda.com/CN/).