Article

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissues

2021-06-21

Abstract excerpt

<title>Abstract</title> <p>Newly developed technologies have made it feasible to routinely collect highly multiplexed (20-60 channel) images at subcellular resolution from human tissues for research and diagnostic purposes. Extracting single cell data from such images requires efficient and accurate image segmentation. This starts with identification of nuclei, a challenging problem in tissue imaging that has rec...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 8a83ffc8-c619-51cc-b69a-30ca3283e371
DOI: 10.21203/rs.3.rs-501324/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

UnMICST: Deep learning with real augmentation for robust segmentation of highly multiplexed images of human tissuesDOI 10.21203/rs.3.rs-501324/v1Read

UNSEG: unsupervised segmentation of cells and their nuclei in complex tissue samplesSemantic proximity Read

Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learningSemantic proximity Read

Fusion of encoder-decoder deep networks improves delineation of multiple nuclear phenotypes.Semantic proximity Read

NuSeT: A Deep Learning Tool for Reliably Separating and Analyzing Crowded CellsSemantic proximity Read

CellBinDB: A Large-Scale Multimodal Annotated Dataset for Cell Segmentation with Benchmarking of Universal ModelsSemantic proximity Read

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometrySemantic proximity Read

Deep-learning quantified cell-type-specific nuclear morphology predicts genomic instability and prognosis in multiple cancer typesSemantic proximity Read

Morphology-Aware Profiling of Highly Multiplexed Tissue Images using Variational AutoencodersSemantic proximity Read

A Self-Supervised Learning Approach for High Throughput and High Content Cell SegmentationSemantic proximity Read

A Self-Supervised Learning Approach for High Throughput and High Content Cell SegmentationSemantic proximity Read

Evaluation of cell segmentation methods without reference segmentationsSemantic proximity Read

Ensemble Processing and Synthetic Image Generation for Abnormally Shaped Nuclei SegmentationSemantic proximity Read