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Detection of pulmonary micronodules in computed tomography images and false positive reduction using 3D convolutional neural networks
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0003-3557-4947
Tallinn University of Technology.
Tallinn University of Technology.
Tallinn University of Technology.
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2019 (English)In: International journal of imaging systems and technology (Print), ISSN 0899-9457, E-ISSN 1098-1098, ISSN 0899-9457Article in journal (Refereed) Published
Abstract [en]

Manual detection of small uncalcified pulmonary nodules (diameter <4 mm) in thoracic computed tomography (CT) scans is a tedious and error‐prone task. Automatic detection of disperse micronodules is, thus, highly desirable for improved characterization of the fatal and incurable occupational pulmonary diseases. Here, we present a novel computer‐assisted detection (CAD) scheme specifically dedicated to detect micronodules. The proposed scheme consists of a candidate‐screening module and a false positive (FP) reduction module. The candidate‐screening module is initiated by a lung segmentation algorithm and is followed by a combination of 2D/3D features‐based thresholding parameters to identify plausible micronodules. The FP reduction module employs a 3D convolutional neural network (CNN) to classify each identified candidate. It automatically encodes the discriminative representations by exploiting the volumetric information of each candidate. A set of 872 micro‐nodules in 598 CT scans marked by at least two radiologists are extracted from the Lung Image Database Consortium and Image Database Resource Initiative to test our CAD scheme. The CAD scheme achieves a detection sensitivity of 86.7% (756/872) with only 8 FPs/scan and an AUC of 0.98. Our proposed CAD scheme efficiently identifies micronodules in thoracic scans with only a small number of FPs. Our experimental results provide evidence that the automatically generated features by the 3D CNN are highly discriminant, thus making it a well‐suited FP reduction module of a CAD scheme.

Place, publisher, year, edition, pages
2019.
National Category
Computer Vision and Robotics (Autonomous Systems)
Research subject
Computerized Image Analysis
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URN: urn:nbn:se:uu:diva-403431DOI: 10.1002/ima.22373OAI: oai:DiVA.org:uu-403431DiVA, id: diva2:1389003
Available from: 2020-01-28 Created: 2020-01-28 Last updated: 2020-01-31Bibliographically approved

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Sintorn, Ida-Maria

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Gupta, AnindyaSintorn, Ida-Maria
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Division of Visual Information and InteractionComputerized Image Analysis and Human-Computer InteractionScience for Life Laboratory, SciLifeLab
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International journal of imaging systems and technology (Print)
Computer Vision and Robotics (Autonomous Systems)

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