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Pregled bibliografske jedinice broj: 969322

Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain


Štajduhar, Andrija; Džaja, Domagoj; Judaš, Miloš; Lončarić, Sven
Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain // Physica. A, Statistical mechanics and its applications (2019) doi:10.1016/j.physa.2018.12.027 (znanstveni, prihvaćen)


Naslov
Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain

Autori
Štajduhar, Andrija ; Džaja, Domagoj ; Judaš, Miloš ; Lončarić, Sven

Vrsta, podvrsta
Radovi u časopisima, znanstveni

Izvornik
Physica. A, Statistical mechanics and its applications (2019)

Status rada
Prihvaćen

Ključne riječi
Neuron detection, Partial differential equations, Brain histology, NeuN

Sažetak
In this paper we propose a new method for automatic detection of neurons in histological sections of the human brain cortex, based on anisotropic diffusion. The anisotropic diffusion is modeled using a partial differential equation (PDE) and is applied to high resolution microscopy images of the brain in order to detect neurons. We also present a novel approach for PDE-model parameter optimization. Due to the issue of inter-observer variability, three human experts have manually annotated neurons in the image dataset on which the proposed method was trained. The average correlation in neuron detection between the human experts was 86.88%, while the average correlation between the proposed method and the human experts is 88.79%, which shows that the proposed method's performance is equal to that of human experts. Moreover, the proposed automatic method provides consistent and reproducible results on all sections and is much faster than human raters or other automatic methods. Additionally, the proposed method's output was verified by a human expert and has correctly distinguished 95.41% of neurons in the test images.

Izvorni jezik
Engleski

Znanstvena područja
Matematika, Interdisciplinarne prirodne znanosti, Računarstvo



POVEZANOST RADA


Ustanove
Fakultet elektrotehnike i računarstva, Zagreb,
Medicinski fakultet, Zagreb

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus


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