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Related Concept Videos

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
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Phase Contrast and Differential Interference Contrast Microscopy01:26

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Phase-Contrast Microscopes
In-phase-contrast microscopes, interference between light directly passing through a cell and light refracted by cellular components is used to create high-contrast, high-resolution images without staining. It is the oldest and simplest type of microscope that creates an image by altering the wavelengths of light rays passing through the specimen. Altered wavelength paths are created using an annular stop in the condenser. The annular stop produces a hollow cone of...
Fixation and Sectioning01:03

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Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Three-Dimensional Microscopy in Microbiology01:28

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Three-dimensional imaging techniques are essential in cell biology, allowing researchers to visualize intricate cellular structures with high resolution. Two prominent methods, Differential Interference Contrast Microscopy (DIC) and Confocal Scanning Laser Microscopy (CSLM), provide distinct advantages for imaging live and thick specimens, respectively.Differential Interference Contrast MicroscopyDIC microscopy enhances contrast in transparent, unstained samples by converting phase...

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Related Experiment Video

Updated: Jun 25, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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Semantic segmentation in skin surface microscopic images with artifacts removal.

Aneesh R P1, Joseph Zacharias1

  • 1College of Engineering Trivandrum, APJ Abdul Kalam Technological University Kerala, Thiruvananthapuram, Kerala, India.

Computers in Biology and Medicine
|August 17, 2024
PubMed
Summary

Removing artifacts like hair and dark corners significantly improves deep learning models for skin lesion segmentation, enhancing diagnostic accuracy in dermoscopy images.

Keywords:
ArtifactsDark cornerDermoscopySemantic segmentationSkin cancerU-net

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare

Background:

  • Skin surface microscopy, including dermoscopy, is vital for diagnosing skin lesions and reducing unnecessary biopsies.
  • Accurate segmentation of skin lesions is crucial for classification, but artifacts in dermoscopy images pose a significant challenge.

Purpose of the Study:

  • To analyze common artifacts in skin surface microscopic images and their impact on deep learning segmentation models.
  • To investigate the novel approach of dark corner detection and removal for improved skin lesion segmentation.

Main Methods:

  • Analysis of common artifacts, specifically hair and dark corners, in dermoscopy images.
  • Evaluation of deep learning model performance before and after artifact removal using PH2, ISIC 2017, and ISIC 2018 datasets.
  • Assessment of segmentation performance using the surface density of artifacts and Dice coefficients.

Main Results:

  • Hair and dark corners are identified as prevalent artifacts affecting segmentation accuracy.
  • Artifact removal led to substantial improvements in Dice coefficients across datasets: 93.49 (86.81) for PH2, 85.86 (79.91) for ISIC 2017, and 75.38 (51.28) for ISIC 2018.
  • The study highlights the critical role of artifact removal in enhancing deep learning efficacy for skin lesion segmentation.

Conclusions:

  • Artifact removal is essential for optimizing deep learning-based skin lesion segmentation.
  • The proposed methods for detecting and removing hair and dark corners significantly boost segmentation performance.
  • Improved segmentation accuracy has the potential to enhance clinical diagnostic performance for pigmented skin lesions.