Fast Lung Localization in Computed Tomography by a 1D Detection Network

Vladislav Proskurov, Anvar Kurmukov, Maxim Pisov, Mikhail Belyaev

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep learning models performed very well in many medical image analysis tasks. However, the majority of these results had been obtained on carefully selected datasets. At the same time, the real clinical flow of Computed Tomography studies often contains series with different properties. We address a particular discrepancy related to a much larger scanning interval, e.g., a single series for thorax, abdomen, and pelvis. We propose to use 1D body organ detection for coarse organ localization on thorax-abdomen CT scans. Localized segments, containing volumes of interests, could be further processed by a heavier task-specific network. We convert 3D CT images into multi-channel 2D coronal images, thus drastically decreasing the dimensionality of the data. We next train a conventional U-net like architecture to solve the task of body part regression and build simple threshold rules to localize lungs along the coronal plane. Additionally, this approach allows for the detection of organs only partially presented in the image. Our network was trained on 20 thousand thorax-abdomen volume segments and validated on three separate datasets. It shows high localization accuracy, stability across datasets and processes a high-resolution CT volume in no more than 200 ms.

Original languageEnglish
Title of host publicationProceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages173-176
Number of pages4
ISBN (Electronic)9781728176918
DOIs
Publication statusPublished - 13 May 2021
Event2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021 - Yekaterinburg, Russian Federation
Duration: 13 May 202114 May 2021

Publication series

NameProceedings - 2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021

Conference

Conference2021 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2021
Country/TerritoryRussian Federation
CityYekaterinburg
Period13/05/2114/05/21

Keywords

  • body-part regression
  • deep-learning
  • medical imaging
  • organ detection

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