LitCovid-sentences  

PMC:7782580 / 46628-47999 JSONTXT

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T343 0-19 Sentence denotes Image preprocessing
T344 20-202 Sentence denotes All image data (X-data and CT-data) in the DICOM format were loaded using the Pydicom library (version 1.4.0) and processed as arrays using the Numpy library (version 1.16.0).X-data:
T345 203-386 Sentence denotes The two-dimensional array (x axis and y axis) of the image of the X-data (size of 512 × 512) was normalized to pixel values of 0–255 and stored in png format using the OpenCV library.
T346 387-455 Sentence denotes Each preprocessed image was resized to 512 × 512 and had 3 channels.
T347 456-464 Sentence denotes CT-data:
T348 465-626 Sentence denotes The array of the CT-data was three-dimensional (x axis, y axis, and z axis), and the length of the z axis was ~300, which represented the number of image slices.
T349 627-703 Sentence denotes Each image slice was two-dimensional (x axis and y axis, size of 512 × 512).
T350 704-891 Sentence denotes As shown in Fig. 1b, the array of the image was divided into three groups in the z axis direction, and each group contained 100 image slices (each case was resampled to 300 image slices).
T351 892-1022 Sentence denotes The image slices in each group were processed using a window center of −600 and a window width of 2000 to extract the lung tissue.
T352 1023-1161 Sentence denotes The images of the CT-data with 300 image slices were normalized to pixel values of 0–255 and stored in npy format using the Numpy library.
T353 1162-1371 Sentence denotes A convolution filter was applied with three 1 × 1 convolution kernels to preprocess the CT-data, which is a trainable layer with the aim of normalizing the input; the image size was 512 × 512, with 3 channels.