It involves raw data and preprocessed files used for statistical analysis and the training of computational models. Please see the readme.txt files under each folder to get further information about the files inside that folder.
In this research, we developed a high throughput method to systematically map functional connections from the dorsal cortex to the thalamus in awake mice by combing optogenetic inactivation with multichannel recording.
Here, we provide:
1. Raw data of multi-units and single-units obtained in this study.
2. MATLAB codes for data analysis.
Data and images for surrogate-adjoint refine based global optimization method Surrogate-adjoint refine based global optimization method combining with multi-stage fuzzy clustering space reduction strategy for expensive problems are included in the attachment.
This repository includes a solidWorks (2019) CAD model of a research quality double pendulum. Additionally, we have provided video data with trackers recorded at 1000 FPS and Encoder data to validate the video data analysis.
This dataset is a seamless, high resolution (5m cell size) bathymetry model of Lord Howe Island and Balls Pyramid. This dataset provides detailed depth information from the island shoreline to the shelf drop off.
This bathymetry dataset integrates multibeam echo sounder data together with depth derived from satellite imagery. Multibeam echo sounder data were acquired aboard two voyages on the CSIRO Marine National Facility R.V. Southern Surveyor in 2008 and 2013. Shallow data was derived from World View II satellite imagery of Lord Howe Island collected in 2013. Inshore depth data around Balls Pyramid derived from Quickbird imagery (collected in 2009) was excluded from this dataset. Gaps in data coverage were interpolated using Natural Neighbor in ArcGIS 10.4. Data were clipped at 0 to 300 m depth.
Full description of methods is outlined in the following open-access publication, accessible by the following link: http://www.mdpi.com/2076-3263/8/1/11/htm
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.