Semantic Lung Segmentation Using Convolutional Neural Networks

  • 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.

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Author:Ching-Sheng Chang, Jin-Fa Lin, Ming-Ching LeeORCiD, Christoph PalmORCiDGND
Parent Title (German):Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin
Publisher:Springer Vieweg
Place of publication:Wiesbaden
Editor:Thomas Tolxdorff, Thomas M. Deserno, Heinz Handels, Andreas Maier, Klaus H. Maier-Hein, Christoph Palm
Document Type:conference proceeding (article)
Year of first Publication:2020
Release Date:2020/04/30
Tag:Chest X-Ray; Deep Learning; Encoder-Decoder Network
GND Keyword:Neuronales Netz; Segmentierung; Brustkorb
First Page:75
Last Page:80
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Fakultät Informatik und Mathematik / Regensburg Medical Image Computing (ReMIC)
research focus:Lebenswissenschaften und Ethik