TY - CONF A1 - Chang, Ching-Sheng A1 - Lin, Jin-Fa A1 - Lee, Ming-Ching A1 - Palm, Christoph A2 - Tolxdorff, Thomas A2 - Deserno, Thomas M. A2 - Handels, Heinz A2 - Maier, Andreas A2 - Maier-Hein, Klaus H. A2 - Palm, Christoph T1 - Semantic Lung Segmentation Using Convolutional Neural Networks T2 - Bildverarbeitung für die Medizin 2020. Algorithmen - Systeme - Anwendungen. Proceedings des Workshops vom 15. bis 17. März 2020 in Berlin N2 - 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. KW - Neuronales Netz KW - Segmentierung KW - Brustkorb KW - Deep Learning KW - Encoder-Decoder Network KW - Chest X-Ray Y1 - 2020 UR - https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/348 SN - 978-3-658-29266-9 SP - 75 EP - 80 PB - Springer Vieweg CY - Wiesbaden ER -