Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network: Proof-of-Concept Study.
Am J Pathol
; 191(3): 463-474, 2021 03.
Article
en En
| MEDLINE
| ID: mdl-33345996
ABSTRACT
In the field of pathology, micro-computed tomography (micro-CT) has become an attractive imaging modality because it enables full analysis of the three-dimensional characteristics of a tissue sample or organ in a noninvasive manner. However, because of the complexity of the three-dimensional information, understanding would be improved by development of analytical methods and software such as those implemented for clinical CT. As the accurate identification of tissue components is critical for this purpose, we have developed a deep neural network (DNN) to analyze whole-tissue images (WTIs) and whole-block images (WBIs) of neoplastic cancer tissue using micro-CT. The aim of this study was to segment vessels from WTIs and WBIs in a volumetric segmentation method using DNN. To accelerate the segmentation process while retaining accuracy, a convolutional block in DNN was improved by introducing a residual inception block. Three colorectal tissue samples were collected and one WTI and 70 WBIs were acquired by a micro-CT scanner. The implemented segmentation method was then tested on the WTI and WBIs. As a proof-of-concept study, our method successfully segmented the vessels on all WTI and WBIs of the colorectal tissue sample. In addition, despite the large size of the images for analysis, all segmentation processes were completed in 10 minutes.
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Asunto principal:
Programas Informáticos
/
Adhesión en Parafina
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Redes Neurales de la Computación
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Imagenología Tridimensional
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Microtomografía por Rayos X
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Am J Pathol
Año:
2021
Tipo del documento:
Article