DOI: 10.7941/d1sp93
title: Segmented high-resolution transmission electron microscopy images of nanoparticles

description: A collection of high-resolution transmission electron microscopy (HRTEM)
 images of crystalline nanoparticles on amorphous substrates and their
 corresponding segmentation maps, including 407 raw camera images and
 segmentation maps; as well as 13 curated datasets created from this larger
 repository. Images cover a variety of microscope magnifications
 (0.02-0.042 nm/pixel), electron dosages (80-884 e/Å2), nanoparticle
 diameters (2.2-20 nm), nanoparticle material (Au, Ag, CdSe), and substrate
 background (ultrathin C, SiN), and have been acquired over multiple
 microscope sessions. Segmentation maps were created by a single human
 labeler. Digital images are provided to aid in viewing data. 
 Datasets have each been curated by metadata characteristics, including
 acquisition date, microscope magnification, electron dosage, nanoparticle
 material, and nanoparticle diameter. The datasets have been processed from
 raw image data and converted into a format conducive for machine learning
 training, including flat field correction, pixel value standardization,
 and image patching.
description: TEM samples were created by dropcasting commercially purchased
 nanoparticles on either an ultrathin C or SiN grid. HRTEM images of
 nanoparticles were collected on a TEAM 0.5 aberration-corrected
 transmission electron microscope with a OneView (Gatan) camera at full
 resolution (4096x4096 pixels). Segmentation labels were created by a
 single human labeler using LabelBox. Digital images of the data (under a
 variety of color mapping protocols) were created and uploaded to LabelBox,
 where they were then labeled.  To create datasets,
 images were first selected using metadata criteria (i.e. microscope
 parameters, nanoparticle characteristics), and then processed into a
 dataset. Processing included (in order): 1) Removal of x-ray artifacts. 2)
 Flat-field correction. 3) Image standardization. 4) Dividing into smaller
 patches. 5) Removal of majority-background patches. X-ray artifacts were
 removed by averaging the surrounding pixels of outlier points above a
 certain threshold (1500 counts) above the image mean. For flat field
 correction, the uneven illumination was estimated using iterative weighted
 linear regression to a 2D Bezier basis (n=2, m=2) and divided out. Images
 were then individually standardized (mean=0, std=1). The original
 4096x4096 pixel images and corresponding labels were then divided into 64
 512x512 pixel patches, and patches that consisted of mostly substrate were
 removed. For more details, see our code here. 
description: Raw image data are saved under the dm3 format, which can be
 opened by Digital Micrograph (also known as Gatan Microscopy Suite),
 ImageJ/Fiji, or using the ncempy Python package. The raw data are stored
 in folders, where each folder generally corresponds to a single microscope
 session. raw_data_metadata.csv provides the metadata and locations for all
 of the image files.  Datasets and their corresponding
 labels are stored as h5 files, which can be opened using the h5py Python
 package. Image files are stored under the key 'images' and
 segmentation labels are stored under the key 'labels'.
 Processed_datasets_metadata.csv provides the metadata for the processed
 datasets and their associated segmentation label files. For an example of
 opening and using these datasets, see our code here.