# Mixed-Sized Biomedical Image Segmentation Based on U-Net Architectures
## Appendix A  
**Listing A1. ** Loading CT-Scan into the related tensors starting from the file paths. ```
1 defprocess_path(filename):
2 patient_bytes=tf.io.read_file(filename)
3 patient_image=tfio. 4 image. 5 decode_dicom_image(patient_bytes,
6 color_dim=False,
7 on_error='skip',
8 scale='preserve',
9 dtype=tf.uint16,
10 name=None)
11 tf.cast(patient_image,tf.int32,name=None)
12 patient_image=tf.image.resize(patient_image,(D,D))
13 patient_image=tf.squeeze(patient_image,axis=0)
14 mask_path=tf.strings.regex_replace(filename,
15'PATIENT_DICOM',
16'MASK_DICOM/liver')
17 mask_bytes=tf.io.read_file(mask_path)
18 mask_image=tfio.image.decode_dicom_image(mask_bytes,
19 scale='auto',
20 on_error='lossy',
21 dtype=tf.uint8)
22 mask_image=tf.squeeze(mask_image,axis=0)
23 intercept_tag=tfio. 24 image. 25 decode_dicom_data(patient_bytes,
26 tags=np.uint32(int(\"00281052\",
2716)))
28 intercept=tf.strings.to_number(intercept_tag,tf.float32)
29 slope_tag=tfio. 30 image. 31 decode_dicom_data(patient_bytes,
32 tags=np.uint32(int(\"00281053\",
3316)))
34 slope=tf.strings.to_number(slope_tag,tf.float32)
35 patient_image=tf. 36 math. 37 add(tf.math.multiply(patient_image,slope),
38 intercept)/100.-10. 39 returnpatient_image,mask_image

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