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.
In the datasets, the scale of organizational culture based on eleven items of primary cultural values (PCV) and nine items of secondary cultural values (SCV). Work motivation variables in the datasets used four items of motive motivation (MM), seven items of expectation motivation (ME), and with nine items of incentive motivation (IM). Interpersonal communication variables were seven items of social sensitivity (SS), nine items of social insight (SI), and four items of social communication skills (SCM). The items, including labels and ratings, will be explained later, with more comprehensive details as supplementary material (see "Annotated Questionnaire" in supplementary material). This was because each item had different rankings and choices. To measure teacher performance used three scales from the book of effective teacher performance. The teacher performance scale used three variables of thirteen items of the learning process (LL), four items of scientific work (SW), and three items of service (S).
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.