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Related Concept Videos

Pigmentation01:19

Pigmentation

The color of the skin is influenced by a number of pigments, including melanin, carotene, and hemoglobin. Recall that melanin is produced by cells called melanocytes, which are found scattered throughout the stratum basale of the epidermis. The melanin is transferred to the keratinocytes via melanosomes.
Melanin occurs in two primary forms: eumelanin that provides black and brown pigment and pheomelanin that provides red color. Dark-skinned individuals produce more melanin than those with pale...

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PIGMENT: A deep learning framework for Porcine Immunohistochemistry seGMENTation.

Pushkar Ambastha1, Javid Dadashkarimi2, Sai Krishna C Annavazala2

  • 1Indian Institute of Technology Guwahati, India.

Biorxiv : the Preprint Server for Biology
|July 3, 2026
PubMed
Summary

Automated analysis of traumatic brain injury axonal damage is now possible with PIGMENT, a deep learning tool that quanties amyloid precursor protein (APP) pathology in histology. This framework enables scalable, reproducible mapping of injury extent and spatial distribution.

Keywords:
SegFormeramyloid precursor proteindata augmentationdeep learninghistology segmentationimmunohistochemistrytraumatic axonal injurytraumatic brain injury

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computational Pathology

Background:

  • Traumatic brain injury (TBI) causes axonal damage, detectable via amyloid precursor protein (APP) immunohistochemistry.
  • Quantifying APP pathology is challenging due to manual annotation limitations (time, variability, scalability).
  • Accurate quantification is crucial for aligning histological data with neuroimaging findings.

Purpose of the Study:

  • To introduce PIGMENT, a deep learning framework for automated segmentation and quantification of APP pathology in porcine white matter histology.
  • To develop an annotation-efficient method addressing the bottleneck in APP pathology assessment.
  • To create spatially resolved APP burden maps for TBI research.

Main Methods:

  • PIGMENT utilizes a SegFormer-B0 architecture trained on expert-annotated histology tiles.
  • Employs APP-specific data augmentation to handle sparse, fragmented, and variable pathology.
  • Evaluated using an instance-level detection rate on held-out APP-stained data.

Main Results:

  • PIGMENT achieved a mean instance-level detection rate of 0.86 on held-out data.
  • Training with diverse animal data improved detection rates under limited-label conditions.
  • The framework successfully generated whole-section APP burden maps.

Conclusions:

  • PIGMENT offers a scalable and reproducible solution for quantifying axonal injury in TBI.
  • Automated APP pathology mapping facilitates alignment with imaging-derived measures.
  • Annotation diversity is key for effective deep learning in limited-label histology.