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Related Concept Videos

Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Determining the Plane of Cell Division02:13

Determining the Plane of Cell Division

Positioning the cell division plane is a critical step during development and cell differentiation, particularly during mitosis when the plane is essential for determining the size of the two daughter cells. The cell division plane is perpendicular to the plane of chromosome segregation, but different types of organisms have different cell division mechanisms to suit their morphology and function. 
Animal cells
In animal cells, the cleavage furrow forms along the plane of cell division starting...
Determining the Plane of Cell Division02:13

Determining the Plane of Cell Division

Positioning the cell division plane is a critical step during development and cell differentiation, particularly during mitosis when the plane is essential for determining the size of the two daughter cells. The cell division plane is perpendicular to the plane of chromosome segregation, but different types of organisms have different cell division mechanisms to suit their morphology and function. 
Animal cells
In animal cells, the cleavage furrow forms along the plane of cell division starting...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Lineage Commitment01:21

Lineage Commitment

Commitment is the  process whereby stem cells:

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Updated: Jun 4, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Published on: August 13, 2014

Cell Segmentation as Strategic Decision Making.

Yunshan Zhong1, Xianwen Ren2,3

  • 1Changping Laboratory, Beijing, China.

Research (Washington, D.C.)
|June 3, 2026
PubMed
Summary
This summary is machine-generated.

RedeFISH, a new deep learning method, accurately segments cells in spatial transcriptomics (ST) without staining. It aligns gene expression profiles for precise cell delineation and enables whole-transcriptome imputation for cancer research.

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Area of Science:

  • Spatial transcriptomics
  • Computational biology
  • Machine learning in genomics

Background:

  • Current cell segmentation in spatial transcriptomics (ST) relies on staining or transcript clustering, often limited by manual annotations and staining quality.
  • Existing methods struggle with accurate alignment to true transcriptomic profiles, impacting downstream analysis.

Purpose of the Study:

  • To develop a novel, staining-free cell segmentation framework for ST that directly utilizes transcriptomic data.
  • To improve the accuracy, robustness, and transcriptome-alignment of cell segmentation in ST.
  • To enable whole-transcriptome imputation from sparse ST data for comprehensive spatial biology insights.

Main Methods:

  • Introduced RedeFISH, a reinforcement learning-based framework for cell segmentation as a sequential decision-making process.
  • Iteratively optimized transcript assignment policy to align segmented cell expression with single-cell expression profiles.
  • Developed a method for whole-transcriptome imputation using spatially guided transfer from scRNA-seq data.

Main Results:

  • RedeFISH achieved improved cosine similarity (9.7% avg.) and reduced RMSE (13.9%) in segmented cell expression profiles compared to state-of-the-art methods.
  • Demonstrated increased agreement (19%) between segmented and ground-truth cell regions, indicating enhanced accuracy and robustness.
  • Successfully imputed whole-transcriptome profiles, enabling in situ prioritization of regulators in spatial niches and analysis of developmental processes in cancer.

Conclusions:

  • RedeFISH offers a robust, staining-free, and transcriptome-informed approach for cell segmentation in ST, overcoming key limitations of existing methods.
  • The framework enhances the accuracy of cell delineation and enables comprehensive molecular profiling of spatial niches and developmental trajectories.
  • This work paves the way for unbiased in situ analysis of complex biological processes, including cancer development, using spatial transcriptomics.