Work

Multimodal Biomedical Dataset for Evaluating Registration Methods (Full-size TMA Cores)

Year: 2021

Type: dataset

Abstract: The dataset consists of 206 aligned Bright-Field (BF) and Second-Harmonic Generation (SHG) images. All images are of the same size of 2048x2048 pixels. The training set contains 40 image pairs. The validation set 1 contains 25 image pairs for tuning the training hyperparameters. The validation set 2 contains 7 image pairs for tuning the registration method. The test set contains 134 image pairs for evaluation. Example of the naming convention in the test set: 1B_A1_BF.tif: image in Bright-Field 1B_A1_SHG.tif: image in Second-Harmonic Generation Example of the naming convention in the train/validation sets: 1B_A2_WB.tif: image in Bright-Field 1B_A2_MI.tif: image in Second-Harmonic Generation The dataset was originally produced by the authors of Non-disruptive collagen characterization in clinical histopathology using cross-modality image synthesis (https://www.nature.com/articles/s42003-020-01151-5).

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.4550299

Authors: Eliceiri, Kevin, Li, Bin, Keikhosravi, Adib

Institutions: University of Wisconsin–Madison, National Institutes of Health

Language: English

Topic: Medical Image Segmentation Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W4393768290

Cites:

Cited by: 3

Related to: 10