CHiMP Detector Datasets: Images of Sitting Drop Protein Crystallisation Experiments with Associated Image Masks of Drops and Crystals

The CHiMP Detector Datasets consist of images of protein crystallisation experiments along with corresponding zipped NumPy archive files (.npz). All images have had their histograms adjusted using the Contrast Limited Adaptive Histogram Equalization ((CLAHE) algorithm using the OpenCV library with grid size of 12 and are in JPEG format. The .npz files contain class labels and instance segmentation masks for both the experimental droplets and any crystals that an expert annotator has deemed to be interesting/mountable. To class labels and masks can be loaded in the following way: import numpy as np # load in the mask and class label list from .npz file located at mask_path mask_file = np.load(mask_path) masks = list(mask_file["masks"].astype(int)) class_labels = list(mask_file["class_labels"]) There are two datasets within this archive: The VMXi CHiMP Detector Dataset. This consists of 237 images of resolution 1688 × 1352 pixels with corresponding masks. These images were collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the VMXi experimental facility at Diamond Light Source, UK. These images and masks were used to train the VMXi CHiMP (Crystal Hits in My Plate) Detector network that performs object detection and instance segmentation of crystals in experimental micrographs using a Mask-R-CNN architecture. The files "vmxi_detector_training.csv" and "vmxi_detector_validation.csv" provide the filenames of the members of the training and validation sets respectively. The XChem CHiMP Detector Dataset. This consists of 350 images of resolution 1024 × 1224 pixels with corresponding masks. These images were collected on a Rock Imager 1000 (Formulatrix, USA) automated microplate imager at the Crystallisation Facility@Harwell, located in the Research Complex at Harwell (RCaH). In addition to the images in the VMXi CHiMP Detector, these images were used to train the XChem CHiMP (Crystal Hits in My Plate) Detector network that performs object detection and instance segmentation of masks and crystals in experimental micrographs using a Mask-R-CNN architecture. The files "xchem_detector_training.csv" and "xchem_detector_validation.csv" provide the filenames of the members of the training and validation sets respectively.