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Automatic segmentation and reconstruction of intracellular compartments in volumetric electron microscopy data
ID
Žerovnik Mekuč, Manca
(
Avtor
),
ID
Bohak, Ciril
(
Avtor
),
ID
Boneš, Eva
(
Avtor
),
ID
Hudoklin, Samo
(
Avtor
),
ID
Romih, Rok
(
Avtor
),
ID
Marolt, Matija
(
Avtor
)
PDF - Predstavitvena datoteka,
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(4,21 MB)
MD5: 6704567650B83635BC38B75451699F6B
URL - Izvorni URL, za dostop obiščite
https://www.sciencedirect.com/science/article/pii/S0169260722003418
Galerija slik
Izvleček
Background and objectives: In recent years, electron microscopy is enabling the acquisition of volumetric data with resolving power to directly observe the ultrastructure of intracellular compartments. New insights and knowledge about cell processes that are offered by such data require a comprehensive analysis which is limited by the time-consuming manual segmentation and reconstruction methods. Method: We present methods for automatic segmentation, reconstruction, and analysis of intracellular compartments from volumetric data obtained by the dual-beam electron microscopy. We specifically address segmentation of fusiform vesicles and the Golgi apparatus, reconstruction of mitochondria and fusiform vesicles, and morphological analysis of the reconstructed mitochondria. Results and conclusion: Evaluation on the public UroCell dataset demonstrated high accuracy of the proposed methods for segmentation of fusiform vesicles and the Golgi apparatus, as well as for reconstruction of mitochondria and analysis of their shapes, while reconstruction of fusiform vesicles proved to be more challenging. We published an extension of the UroCell dataset with all of the data used in this work, to further contribute to research on automatic analysis of the ultrastructure of intracellular compartments.
Jezik:
Angleški jezik
Ključne besede:
reconstruction
,
instance segmentation
,
electron microscopy
,
mitochondria
,
Golgi apparatus
,
fusiform vesicles
,
urothelium
,
deep learning
,
intracellular compartments
Vrsta gradiva:
Članek v reviji
Tipologija:
1.01 - Izvirni znanstveni članek
Organizacija:
FRI - Fakulteta za računalništvo in informatiko
MF - Medicinska fakulteta
Status publikacije:
Objavljeno
Različica publikacije:
Objavljena publikacija
Leto izida:
2022
Št. strani:
14 str.
Številčenje:
Vol. 223, art. 106959
PID:
20.500.12556/RUL-137728
UDK:
004.8:537.533.35
ISSN pri članku:
0169-2607
DOI:
10.1016/j.cmpb.2022.106959
COBISS.SI-ID:
112962819
Datum objave v RUL:
29.06.2022
Število ogledov:
1159
Število prenosov:
136
Metapodatki:
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Objavi na:
Gradivo je del revije
Naslov:
Computer methods and programs in biomedicine
Skrajšan naslov:
Comput. methods programs biomed.
Založnik:
Elsevier
ISSN:
0169-2607
COBISS.SI-ID:
25260032
Licence
Licenca:
CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
Povezava:
http://creativecommons.org/licenses/by/4.0/deed.sl
Opis:
To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.
Sekundarni jezik
Jezik:
Slovenski jezik
Ključne besede:
mikroskopija
,
mitohondriji
,
Golgijev aparat
,
fuziformni vezikli
,
urotelij
,
globoko učenje
Projekti
Financer:
ARRS - Agencija za raziskovalno dejavnost Republike Slovenije
Številka projekta:
P3-0108
Naslov:
Diferenciacija urotelijskih celic
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