<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>348</id>
    <completedYear/>
    <publishedYear>2020</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>75</pageFirst>
    <pageLast>80</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>Springer Vieweg</publisherName>
    <publisherPlace>Wiesbaden</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2020-04-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Semantic Lung Segmentation Using Convolutional Neural Networks</title>
    <abstract language="eng">Chest X-Ray (CXR) images as part of a non-invasive diagnosis method are commonly used in today’s medical workflow. In traditional methods, physicians usually use their experience to interpret CXR images, however, there is a large interobserver variance. Computer vision may be used as a standard for assisted diagnosis. In this study, we applied an encoder-decoder neural network architecture for automatic lung region detection. We compared a three-class approach (left lung, right lung, background) and a two-class approach (lung, background). The differentiation of left and right lungs as direct result of a semantic segmentation on basis of neural nets rather than post-processing a lung-background segmentation is done here for the first time. Our evaluation was done on the NIH Chest X-ray dataset, from which 1736 images were extracted and manually annotated. We achieved 94:9% mIoU and 92% mIoU as segmentation quality measures for the two-class-model and the three-class-model, respectively. This result is very promising for the segmentation of lung regions having the simultaneous classification of left and right lung in mind.</abstract>
    <parentTitle language="deu">Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin</parentTitle>
    <identifier type="isbn">978-3-658-29266-9</identifier>
    <identifier type="doi">10.1007/978-3-658-29267-6_17</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <author>Ching-Sheng Chang</author>
    <author>Jin-Fa Lin</author>
    <author>Ming-Ching Lee</author>
    <author>Christoph Palm</author>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Neuronales Netz</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Segmentierung</value>
    </subject>
    <subject>
      <language>deu</language>
      <type>swd</type>
      <value>Brustkorb</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Encoder-Decoder Network</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Chest X-Ray</value>
    </subject>
    <collection role="ddc" number="616">Krankheiten</collection>
    <collection role="institutes" number="FakIM">Fakultät Informatik und Mathematik</collection>
    <collection role="institutes" number="RCBE">Regensburg Center of Biomedical Engineering - RCBE</collection>
    <collection role="persons" number="palmremic">Palm, Christoph (Prof. Dr.) - ReMIC</collection>
    <collection role="othforschungsschwerpunkt" number="16314">Lebenswissenschaften und Ethik</collection>
    <collection role="institutes" number="">Labor Regensburg Medical Image Computing (ReMIC)</collection>
  </doc>
</export-example>
