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Related Concept Videos

Olfaction01:25

Olfaction

46.2K
The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...
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Physiology of Smell and Olfactory Pathway01:20

Physiology of Smell and Olfactory Pathway

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Humans detect odors with the help of specialized cells located in the upper part of the nasal cavity, called olfactory receptor neurons (ORNs). ORNs possess hair-like structures called cilia, which are receptive to sensations from the inhaled air. When an odorant molecule binds to a specific receptor on the cell of the cilia, it leads to a series of events that ultimately cause the ORN to send electrical signals to the olfactory bulb in the brain through the olfactory nerves.
The olfactory...
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Olfactory Receptors: Location and Structure01:03

Olfactory Receptors: Location and Structure

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The process of olfaction, also known as the sense of smell, is a sophisticated chemical response system. The specialized sensory neurons that facilitate this process, known as olfactory receptor neurons, are situated in an upper segment of the nasal cavity, known as the olfactory epithelium. Olfactory sensory neurons are bipolar, with their dendrites extending from the epithelium's apex into the mucus that lines the nasal cavity. Airborne molecules, when inhaled, traverse the olfactory...
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Related Experiment Video

Updated: Oct 18, 2025

Imaging Odor-Evoked Activities in the Mouse Olfactory Bulb using Optical Reflectance and Autofluorescence Signals
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Imaging Odor-Evoked Activities in the Mouse Olfactory Bulb using Optical Reflectance and Autofluorescence Signals

Published on: October 31, 2011

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Automatic Segmentation of the Olfactory Bulb.

Dmitriy Desser1, Francisca Assunção2, Xiaoguang Yan1

  • 1Smell & Taste Clinic, Department of Otorhinolaryngology, Technische Universität, 01307 Dresden, Germany.

Brain Sciences
|September 28, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an automated tool for measuring olfactory bulb volume (OBV), crucial for diagnosing olfactory disorders and neurodegenerative diseases. The tool provides fast, accurate, and reliable OBV measurements, matching manual segmentation accuracy.

Keywords:
deep learningolfactory bulbolfactory losssegmentation

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Medical Image Analysis

Background:

  • The olfactory bulb (OB) volume is a key biomarker for olfactory function and neurodegenerative diseases like Alzheimer's.
  • Manual segmentation of the OB for volume measurement is time-consuming, subjective, and hinders large-scale research.
  • There is a need for an automated, reliable method for OB volume quantification.

Purpose of the Study:

  • To develop and validate a novel, fully automated methodological framework for unbiased olfactory bulb volume (OBV) measurement.
  • To create a tool that accurately and quickly segments the OB using multimodal MRI data.
  • To enable efficient processing of large datasets for olfactory research and clinical applications.

Main Methods:

  • Developed a four-step automated algorithm: multimodal data co-registration (T1/T2 MRI), template-based OB localization, bounding box construction, and 3D-U-Net segmentation.
  • Trained the neural network using four datasets including T1 and high-resolution T2 MRI scans from patients with olfactory loss and healthy controls.
  • Manual segmentation labels were created by two independent, blinded raters to establish a gold standard.

Main Results:

  • The automated tool achieved a mean Dice coefficient (DC) of 0.77 ± 0.05 on unseen data, comparable to the inter-rater DC of 0.79 ± 0.08.
  • The symmetric surface distance (ASSD) was 0.43 ± 0.10, indicating high spatial agreement.
  • Automated segmentations received manual ratings equivalent to expert manual segmentations, with processing time of 3-5 minutes per subject.

Conclusions:

  • The developed automated tool provides fast, reliable, and accurate olfactory bulb segmentation, matching the performance of manual methods.
  • This ready-to-use tool has potential for immediate clinical implementation in diagnosing and treating olfactory dysfunctions.
  • The framework facilitates large-scale data processing, advancing olfactory system research in health and disease.