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Related Concept Videos

Anatomy of Blood Vessels01:20

Anatomy of Blood Vessels

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The vascular system, an integral part of the circulatory system, comprises various blood vessels that play crucial roles in maintaining the body's homeostasis. These blood vessels form a complex and efficient circulatory network. The three primary categories of blood vessels are the arteries, veins, and capillaries.
Arteries
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Structure of Blood Vessels01:15

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Blood is circulated throughout the human body through a network of blood vessels called the circulatory system. This system includes arteries that transport blood from the heart to various body parts. These arterial pathways divide into smaller vessels until they reach the arterioles, which further split into capillaries. It is within these minuscule capillaries that the exchange of nutrients and waste products takes place. After this exchange, the blood is collected by venules, which fuse to...
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Development of Blood Vessels01:07

Development of Blood Vessels

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The development of the vascular system in a fetus is a complex and intricate process that begins as early as 15 to 16 days post-conception. This process starts outside the embryo, specifically in the mesoderm of the yolk sac, chorion, and connecting stalk. Approximately two days later, the formation of blood vessels occurs within the embryo itself.
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Overview of Blood Vessels01:14

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The human cardiovascular system comprises five primary types of blood vessels: arteries, arterioles, veins, venules, and capillaries, each serving unique functions.
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Convolution Properties II01:17

Convolution Properties II

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The important convolution properties include width, area, differentiation, and integration properties.
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
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Protein Networks02:26

Protein Networks

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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Deep Learning-Based Segmentation of Cryo-Electron Tomograms
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Retinal blood vessel segmentation using fully convolutional network with transfer learning.

Zhexin Jiang1, Hao Zhang1, Yi Wang1

  • 1University of Saskatchewan, Department of Electrical and Computer Engineering, 57 Campus Drive, Saskatoon, Canada S7N 5A9.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|May 19, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method for precise retinal blood vessel segmentation, crucial for diagnosing eye and heart conditions. The approach achieves state-of-the-art accuracy across multiple datasets, demonstrating transfer learning

Keywords:
Deep learningFully convolutional networkPre-trained modelRetinal blood vessel segmentationTransfer learning

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Area of Science:

  • Ophthalmology and Cardiovascular Imaging
  • Medical Image Analysis
  • Deep Learning in Healthcare

Background:

  • Retinal blood vessel segmentation is vital for diagnosing ophthalmological and cardiovascular diseases.
  • Accurate segmentation is a prerequisite for automated and computer-aided diagnostic systems.
  • Current methods face challenges in achieving high accuracy and robustness.

Purpose of the Study:

  • To develop a robust and accurate supervised method for retinal blood vessel segmentation using deep learning.
  • To simplify the segmentation task by focusing on regional vessel element recognition and merging.
  • To validate the effectiveness of transfer learning in medical image analysis.

Main Methods:

  • A supervised method employing a pre-trained fully convolutional network (FCN) via transfer learning.
  • Simplification of segmentation to regional vessel element recognition and result merging.
  • Integration of unsupervised image post-processing techniques for result refinement.

Main Results:

  • Achieved state-of-the-art accuracy in cross-database tests on DRIVE, STARE, CHASE_DB1, and HRF databases.
  • Demonstrated high robustness of the proposed approach across diverse datasets.
  • Validated the effectiveness of transfer learning for medical image segmentation.

Conclusions:

  • The proposed method significantly advances automated retinal blood vessel segmentation.
  • Transfer learning proves effective for deep learning applications in medical imaging.
  • The approach offers a robust solution for improved disease diagnosis.