ArticleThe American journal of pathology2021
Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network: Proof-of-Concept Study.
Article in The American journal of pathology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed, 12 citations in OpenAlex.
- Unveiling Another Dimension: Advanced Visualization of Cancer Invasion and Metastasis via Micro-CT Imaging.Cancers · 2025Article
- Micro-Computed Tomography Whole-Block Imaging Reveals Origin and Path of Rectal Cancer Tumor Deposits: A Pilot Study.Diagnostics (Basel, Switzerland) · 2024Article
- Three-Dimensional Cell Culture Micro-CT Visualization within Collagen Scaffolds in an Aqueous Environment.Cells · 2024Article
- Artificial intelligence-based automated determination in breast and colon cancer and distinction between atypical and typical mitosis using a cloud-based platform.Pathology oncology research : POR · 2024Article
- The Value of Micro-CT in the Diagnosis of Lung Carcinoma: A Radio-Histopathological Perspective.Diagnostics (Basel, Switzerland) · 2023Review
- Yttrium-90 Activity Quantification in PET/CT-Guided Biopsy Specimens from Colorectal Hepatic Metastases Immediately after Transarterial Radioembolization Using Micro-CT and Autoradiography.Journal of vascular and interventional radiology : JVIR · 2023Article
- Imaging Hallmarks of Sarcoma Progression Via X-ray Computed Tomography: Beholding the Flower of Evil.Cancers · 2022Review
- A pilot study of micro-CT-based whole tissue imaging (WTI) on endoscopic submucosal dissection (ESD) specimens.Scientific reports · 2022Article
- Pathological Evaluation of Rectal Cancer Specimens Using Micro-Computed Tomography.Diagnostics (Basel, Switzerland) · 2022Article
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
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.
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