Three-dimensional vessel segmentation in whole-tissue and whole-block imaging using a deep neural network: Proof-of-concept study Journal Article


Authors: Ohnishi, T.; Teplov, A.; Kawata, N.; Ibrahim, K.; Ntiamoah, P.; Firat, C.; Haneishi, H.; Hameed, M.; Shia, J.; Yagi, Y.
Article Title: Three-dimensional vessel segmentation in whole-tissue and whole-block imaging using a deep neural network: Proof-of-concept study
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. © 2021 American Society for Investigative Pathology
Journal Title: American Journal of Pathology
Volume: 191
Issue: 3
ISSN: 0002-9440
Publisher: Elsevier Science, Inc.  
Date Published: 2021-03-01
Start Page: 463
End Page: 474
Language: English
DOI: 10.1016/j.ajpath.2020.12.008
PUBMED: 33345996
PROVIDER: scopus
PMCID: PMC7927274
DOI/URL:
Notes: Article -- Export Date: 1 March 2021 -- Source: Scopus
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MSK Authors
  1. Meera Hameed
    214 Hameed
  2. Jinru Shia
    627 Shia
  3. Alexei Teplov
    28 Teplov
  4. Yukako Yagi
    53 Yagi
  5. Kareem Salah Ibrahim
    20 Ibrahim
  6. Canan Firat
    26 Firat
  7. Noboru Kawata
    9 Kawata
  8. Takashi Ohnishi
    11 Ohnishi