File size: 3,466 Bytes
35b402a | 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 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 | import os
import nibabel as nib
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
# 输入路径和输出路径
input_folder = "training_data1_v2"
output_folder = "BraTS"
# 定义模态后缀和目标文件夹的模态名称
modalities = {"T1": "t1n", "T1c": "t1c", "T2": "t2w", "FLAIR": "t2f"}
# 创建输出文件夹结构
os.makedirs(os.path.join(output_folder, "train", "image"), exist_ok=True)
# 数据归一化函数（修正除零问题）
def normalize_slice(slice_data):
if np.max(slice_data) == 0:
return np.zeros(slice_data.shape, dtype=np.uint8) # 全零切片
slice_data = slice_data - np.min(slice_data) # 移动到 0
slice_data = slice_data / np.max(slice_data) # 归一化到 [0, 1]
slice_data = (slice_data * 255).astype(np.uint8) # 转换到 [0, 255]
return slice_data
# 标签分配函数
def assign_label(patient_folder):
seg_file = os.path.join(patient_folder, f"{os.path.basename(patient_folder)}-seg.nii.gz")
if not os.path.exists(seg_file):
print(f"分割文件缺失: {seg_file}")
return None # 如果分割文件缺失，返回 None
seg_img = nib.load(seg_file)
seg_data = seg_img.get_fdata()
unique_labels = np.unique(seg_data)
if len(unique_labels) == 1 and unique_labels[0] == 0:
return 0 # 健康人
elif 1 in unique_labels:
return 2 # 存在坏死区域
else:
return 1 # 水肿和增强组织为主
# 处理患者文件夹
def process_patient(patient_folder, patient_id, label):
patient_output_folder = os.path.join(output_folder, "train", "image", f"{patient_id:03d}_{label}")
os.makedirs(patient_output_folder, exist_ok=True)
# 遍历模态
for modality, suffix in modalities.items():
modality_folder = os.path.join(patient_output_folder, modality)
os.makedirs(modality_folder, exist_ok=True)
# 构造文件路径
nii_file = os.path.join(patient_folder, f"{os.path.basename(patient_folder)}-{suffix}.nii.gz")
if not os.path.exists(nii_file):
print(f"缺失文件: {nii_file}")
continue
# 加载 NIfTI 文件
img = nib.load(nii_file)
data = img.get_fdata()
# 遍历切片
for i in range(data.shape[2]): # 假设第三维是切片维度
slice_data = data[:, :, i] # 提取切片
slice_data = normalize_slice(slice_data) # 归一化
# 保存为 PNG
slice_path = os.path.join(modality_folder, f"slice_{i:03d}.png")
plt.imsave(slice_path, slice_data, cmap="gray")
print(f"患者 {patient_id} 数据处理完成！")
# 遍历患者文件夹并处理
patient_folders = [os.path.join(input_folder, folder) for folder in os.listdir(input_folder) if
os.path.isdir(os.path.join(input_folder, folder))]
csv_data = []
for patient_id, patient_folder in enumerate(patient_folders, start=1):
label = assign_label(patient_folder) # 分配标签
if label is None:
continue # 跳过没有分割文件的患者
process_patient(patient_folder, patient_id, label)
# 记录到 CSV
csv_data.append({"ID": patient_id, "Label": label})
# 保存标签 CSV 文件
csv_path = os.path.join(output_folder, "train", "train_labels.csv")
pd.DataFrame(csv_data).to_csv(csv_path, index=False)
print(f"标签文件已保存到: {csv_path}")
print("所有患者数据处理完成！") |