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

Takashi Ohnishi (1,2), Alexei Teplov (2), Noboru Kawata (2,3), Kareem Ibrahim (2), Peter Ntiamoah (2), Canan Firat (2), Hideaki Haneishi (1), Meera Hameed (2), Jinru Shia (2), Yukako Yagi (2)

The American Journal of Pathology, 191, Issue 3, March 2021:463-474. DOI: 10.1016/j.ajpath.2020.12.008


Vessel segmentation; Deep neural network; Micro computed tomography; Whole-block imaging; Whole-tissue imaging


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.

How Our Software Was Used

Dragonfly was used to create superimposed images and to visualize all images.

Author Affiliation

(1) Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba 263-8522, Japan
(2) Department of Pathology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA (3)Division of Endoscopy, Shizuoka Cancer Center, 1007, Shimonagakubo, Nagaizumi-cho, Sunto-gun, Shizuoka 411-8777, Japan

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