Author: Chuansheng Zheng; Xianbo Deng; Qing Fu; Qiang Zhou; Jiapei Feng; Hui Ma; Wenyu Liu; Xinggang Wang
Title: Deep Learning-based Detection for COVID-19 from Chest CT using Weak Label Document date: 2020_3_17
ID: ll4rxd9p_15
Snippet: Preprocessing of 2D UNet All the CT volumes were preprocessed in a unified manner before training the 2D UNet for lung segmentation. First, the unit of measurement was converted to the Hounsfield Unit (HU) and the value was linearly normalized from 16-bit to 8-bit (i.e., 0-255) after determining the threshold of a HU window (e.g., -1 200-600 HU). After that, all the CT volumes were resampled into a same spatial resolution (e.g., 368×368), by whi.....
Document: Preprocessing of 2D UNet All the CT volumes were preprocessed in a unified manner before training the 2D UNet for lung segmentation. First, the unit of measurement was converted to the Hounsfield Unit (HU) and the value was linearly normalized from 16-bit to 8-bit (i.e., 0-255) after determining the threshold of a HU window (e.g., -1 200-600 HU). After that, all the CT volumes were resampled into a same spatial resolution (e.g., 368×368), by which the CT volumes could be aligned without the influence of the cylindrical scanning bounds of CT scanners. This step was applied to the obtained ground-truth lung masks as well.
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