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The Nucleus

The Nucleus

The nucleus is a membrane-bound organelle that acts as a control center in a eukaryotic cell. It contains chromosomal DNA, which controls gene expression and precisely regulates the production of proteins within the cell. In contrast, the DNA inside the mitochondria and chloroplast only carries out functions that are specific to those organelles.
Arrangement of DNA within Nucleus
The regulation of gene expression inside the nucleus is dependent on many factors, including the DNA structure. The...

Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Nuclear Localization Signals and Import

Nuclear Localization Signals and Import

Proteins targeted to the nucleus carry short stretches of amino acid sequences called the nuclear localization signal or NLS. Classical nuclear localization signals are of two types: monopartite and bipartite NLS. Monopartite classical NLS (cNLS) consists of a single cluster of 4-8 amino acids. Bipartite cNLS consists of two clusters of 2-3 amino acids and a 9-12 residue long proline-rich linker bridging the two clusters. Signal clusters are rich in positively charged amino acids such as...

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Related Experiment Video

Updated: Sep 18, 2025

Using Computer Vision Libraries to Streamline Nuclei Quantification

Using Computer Vision Libraries to Streamline Nuclei Quantification

Published on: June 6, 2025

Prompting Vision-Language Model for Nuclei Instance Segmentation and Classification.

Jieru Yao, Guangyu Guo, Zhaohui Zheng

IEEE Transactions on Medical Imaging

|June 25, 2025

Summary

This summary is machine-generated.

PromptNu enhances nuclei instance segmentation and classification in whole slide imaging (WSI) by leveraging vision-language models (VLMs) and prompt engineering. This novel framework reduces reliance on manual annotations for accurate nuclei detection.

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Published on: April 13, 2019

Area of Science:

Digital Pathology
Computational Biology
Artificial Intelligence

Background:

Nuclei instance segmentation and classification are critical yet challenging in whole slide imaging (WSI) analysis.
Current methods often require extensive manual annotation, demanding significant time and expertise.
Vision-Language Models (VLMs) offer a promising alternative by learning from large-scale image-text data without tedious labeling.

Purpose of the Study:

To introduce PromptNu, a novel framework for nuclei instance recognition.
To infuse comprehensive nuclei knowledge into model training using vision-language contrastive learning and prompt engineering.
To overcome the limitations of traditional annotation-dependent methods in WSI analysis.

Main Methods:

Developed multifaceted prompts integrating visual, statistical, and expert nuclear knowledge.
Proposed Prompting Nuclei Representation Learning (PNuRL) for feature extraction.
Introduced Prompting Nuclei Dense Prediction (PNuDP) for integrating VLM expertise into prediction.
Utilized vision-language contrastive learning to enhance nuclei instance recognition.

Main Results:

Demonstrated the effectiveness of PromptNu across six diverse datasets.
Achieved significant improvements in both nuclei instance segmentation and classification tasks.
Validated the framework's performance in various whole slide imaging scenarios.

Conclusions:

PromptNu successfully integrates VLM knowledge and prompt engineering for nuclei analysis in WSI.
The proposed method offers an efficient and effective alternative to manual annotation-driven approaches.
The framework shows strong potential for advancing automated digital pathology.