Pytorch项目，肺癌检测项目之二

永远的HelloWorld2023-12-27 3:06

diameter_dict={}

with open('/xunlian/annotations.csv' ，'r') as f:

for row in list(csv.reader(f) $1:$ ):

series_uid = row $0$

annotationCenter_xyz = tuple( $float(x) for x in row\[1:4$ ])

annotationDiameter_mm = float(row $4$ )

diameter_dict.setdefault(series_uid,\[\]).append(annotationCenter_xyz ,annotationDiameter_mm )

#两个文件存储的中心点坐标距离相差是否超过了结节直径的四分之一

candidateInfo_list.sort(reverse=True)

return candidateInfo_list

导入SimpleITK

import SimpleITK as sitk

class Ct:

def init(self,series_uid):

mhd_path = glob.glob('/xunlian/subset*/{}.mhd'.format(series_uid)) $0$

ct_mhd = sitk.ReadImage(mhd_path)

ct_a = np.array(sitk.GetArrayFromImage(ct_mhd),dtype=np.float32

ct_a.clip(-1000,1000,ct_a)

self.series_uid = series_uid

self.hu_a = ct_a # HU

self.origin_xyz = XyzTuple(*ct_mhd.GetOrigin())

self.vxSize_xyz = XyzTuple(*ct_mhd.GetSpacing())

self.direction_a = np.array(ct_mhd.GetDirection().reshape(3,3))

毫米为单位的坐标称为（X,Y,Z）坐标，以体素为单位的坐标称为（I,R,C）

数据坐标系的转化的代码实现

IrcTuple = collections.namedtuple('IrcTuple', $'index','row','col'$ )

XyzTuple = collections.namedtuple('XyzTuple', $'x','y','z'$ )