Models:
Joseph Gordon
joseph-gordon/
SwinScore
0
Downloads: 1
[d5c425]: / preprocessing.py

Download this file

63 lines (56 with data), 2.2 kB 

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
import os 

import numpy as np

import pandas as pd

from parameters import mkdirs

import pickle

from medpy.io import load



def write_list(L, file):

    # store list in binary file so 'wb' mode

    with open(file, 'wb') as fp:

        pickle.dump(L, fp)

        print('Done writing list into a binary file')



def build_ct_boxes(path):

    df = pd.read_csv(os.path.join(path,'data_table.csv'))

    for label_name in ['label.nii.gz', 'node.nii.gz']:

        X_min, X_max, Y_min, Y_max, Z_min, Z_max = [], [], [], [], [], []

        for patient in df["radiology_folder_name"]:

            print(os.path.join(path, patient, label_name))

            if os.path.isfile(os.path.join(

                    path, patient, label_name)):

                print("it is file")

                label, _ = load(os.path.join(

                        path, patient, label_name))

                x_min, x_max = np.min(np.nonzero(label)[1]), np.max(

                    np.nonzero(label)[1])

                y_min, y_max = np.min(np.nonzero(label)[0]), np.max(

                    np.nonzero(label)[0])

                z_min, z_max = np.min(np.nonzero(label)[2]), np.max(

                    np.nonzero(label)[2])

            else:

                x_min, x_max = 0, 0

                y_min, y_max = 0, 0

                z_min, z_max = 0, 0

            X_min.append(x_min)

            Y_min.append(y_min)

            Z_min.append(z_min)

            X_max.append(x_max)

            Y_max.append(y_max)

            Z_max.append(z_max)

        if label_name == "label.nii.gz":

            df["X_min_tumor"] = X_min

            df["Y_min_tumor"] = Y_min

            df["X_max_tumor"] = X_max

            df["Y_max_tumor"] = Y_max

            df["Z_min_tumor"] = Z_min

            df["Z_max_tumor"] = Z_max



        else:

            df["X_min_lymph"] = X_min

            df["Y_min_lymph"] = Y_min

            df["X_max_lymph"] = X_max

            df["Y_max_lymph"] = Y_max

            df["Z_min_lymph"] = Z_min

            df["Z_max_lymph"] = Z_max



    df.to_csv(os.path.join(path, 'data_table_new.csv'))



    return



path = r"C:\Users\bsong47\Documents\p16_positive_opc_ct\radiology"



build_ct_boxes(path)