Description from Zenodo: "Objective. This work aims to generate realistic anatomical deformations from static patient scans. Specifically, we present a method to generate these deformations/augmentations via deep learning driven respiratory motion simulation that provides the ground truth for validating deformable image registration (DIR) algorithms and driving more accurate deep learning based...

Description from Zenodo: "In this repository we share models trained on a large annotated dataset of PET DOTATATE images using the nnU-Net V1 pipeline. This version of the pipeline can be found here. Given the modularity of the nnU-net framework, we make these models public with the hopes of allowing other institutions to benefit from our pre-training and to improve upon and verify the generalizability...

Description from Figshare: "Files Provided: - denoised.zip,denoised1.zip: denoised MIBI images for the entire cohort. The images are divided into two archive files due to uploading file size limitations - we apologize for the inconvenience. The files are organized by field of view (FOV), with each FOV subfolder containing TIF images for each channel that have undergone low-level processing as described...

Description from GitHub: "This is the repository associated with the article by Morgani et al., A Sprouty4 reporter to monitor FGF/ERK signaling activity in ESC and mice published in Developmental Biology 441:1 pp104:126 (2018) doi.org/10.1016/j.ydbio.2018.06.017 This repository contains the corrected data tables for all preimplantation embryos analyzed in the study, the R scripts used to clean,...

Description from Figshare: "Original immunofluorescence images, raw fluorescence quantification data, corrected data tables and segmentation files for all preimplantation embryos analyzed in the paper A Sprouty4 reporter to monitor FGF/ERK signaling activity in ESCs and mice, by Morgani et al., (2018) Developmental Biology. 441,1 pp104:126 doi.org/10.1016/j.ydbio.2018.06.017"

Description from GitHub: "This directory contains the five equivariant transformer models described in the paper Peter Eastman, Pavan Kumar Behara, David L. Dotson, Raimondas Galvelis, John E. Herr, Josh T. Horton, Yuezhi Mao, John D. Chodera, Benjamin P. Pritchard, Yuanqing Wang, Gianni De Fabritiis, and Thomas E. Markland. "SPICE, A Dataset of Drug-like Molecules and Peptides for Training Machine...