Work

HPA Cell Image Segmentation Dataset

Year: 2021

Type: dataset

Abstract: This deposition contains an annotated image segmentation datasets and pretrained models. This dataset includes annotated cell images obtained from the Human Protein Atlas (http://www.proteinatlas.org), each image contains 4 channels (Microtubules, ER, Nuclei and Protein of Interest). The cells in each image are annotated with polygons and saved into GeoJSON format produced with Kaibu(https://kaibu.org) annotation tool. hpa_cell_segmentation_dataset_v2_512x512_4train_159test.zip is an example dataset for running a deep learning-based interactive annotation tools in ImJoy (https://github.com/imjoy-team/imjoy-interactive-segmentation). hpa_dataset_v2.zip is a full annotate image segmentation dataset other files named *.pth are pytorch model weights which can be loaded via our deep learning library in Python: https://github.com/CellProfiling/HPA-Cell-Segmentation

Source: Figshare

DOI: 10.5281/zenodo.4430892

Authors: Kaimal, Jay, Thul, Peter, Xu, Hao, Ouyang, Wei, Lundberg, Emma

Institutions: Science for Life Laboratory, KTH Royal Institute of Technology

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W4393414049

Cites:

Cited by: 4

Related to: 10