Automatic segmentation and reconstruction of intracellular compartments in volumetric electron microscopy data

Žerovnik Mekuč, Manca (avtor), Bohak, Ciril (avtor), Boneš, Eva (avtor), Hudoklin, Samo (avtor), Romih, Rok (avtor), Marolt, Matija (avtor)

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

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