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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...

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT)

Insensitive Nuclei Enhanced by Polarization Transfer (INEPT) is an advanced Nuclear Magnetic Resonance (NMR) technique specifically designed to detect and enhance the signals of low-abundance nuclei, such as carbon-13 and nitrogen-15, in small molecules. The fundamental principle behind INEPT is the transfer of polarization from a more abundant and highly polarizable nucleus, typically hydrogen-1, to the low-abundance nucleus of interest. This process effectively boosts the NMR signal of the...

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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

Instance-Aware Multi-Task Learning for Nuclei Segmentation.

Wei Lou, Haofeng Li, Guanbin Li

IEEE Transactions on Medical Imaging

|June 25, 2025

Summary

This summary is machine-generated.

This study introduces an instance-aware framework for nuclei segmentation in computational pathology. The novel approach enhances automatic nuclei segmentation by treating each nucleus as an individual entity, improving analysis accuracy.

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Area of Science:

Computational pathology
Medical image analysis
Computer vision

Background:

Nuclei segmentation is crucial for computational pathology.
Existing methods often lack instance-level feature representation.
This limits detailed analysis of individual nuclei.

Purpose of the Study:

To develop an instance-aware multi-task learning framework for nuclei segmentation.
To improve the representation of individual nuclei at the feature level.
To enhance the accuracy of automatic nuclei segmentation.

Main Methods:

Proposed an instance-aware multi-task learning framework with pixel-wise and instance-wise branches.
Introduced an instance-disentangling feature learning module.
Developed a dual-branch unified post-processing algorithm.

Main Results:

Achieved competitive performance on nuclei segmentation benchmarks.
Demonstrated effective capture of positional and visual information for individual nuclei.
Successfully aligned object-level queries with pixel-wise features.

Conclusions:

The proposed framework advances nuclei segmentation in computational pathology.
Instance-aware learning provides superior feature-level representation for individual nuclei.
The method offers a robust solution for automated analysis of cellular structures.