Author: Abdulah, Haikal; Huber, Benjamin; Lal, Sinan; Abdallah, Hassan; Soltanian-Zadeh, Hamid; Gatti, Domenico L.
Title: Lung Segmentation in Chest X-rays with Res-CR-Net Cord-id: qahx2vt6 Document date: 2020_11_14
ID: qahx2vt6
Snippet: Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy p
Document: Deep Neural Networks (DNN) are widely used to carry out segmentation tasks in biomedical images. Most DNNs developed for this purpose are based on some variation of the encoder-decoder U-Net architecture. Here we show that Res-CR-Net, a new type of fully convolutional neural network, which was originally developed for the semantic segmentation of microscopy images, and which does not adopt a U-Net architecture, is very effective at segmenting the lung fields in chest X-rays from either healthy patients or patients with a variety of lung pathologies.
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