文章目录

• 肝脏分割数据集
• 一、预处理
• 二、图像增强
• 三、训练
• 四、测试
• 总结

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肝脏分割数据集

肝脏分割分割主要有三个开源数据集：LiTS17，Sliver07，3DIRCADb

LiTS17包含131个数据，可以用于肝脏及肿瘤的分割

Sliver07包含20个数据，但仅仅包含肝脏，并没有肿瘤的金标准

3DIRCADb包含20个数据，可以用于肝脏及肿瘤的分割

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提示：以下是本篇文章正文内容，下面案例可供参考

一、预处理

这三个肝脏分割数据集都是3D数据，所以如过要进行训练必须要对数据进行切片。

对3D nii文件进行调窗，找到肝脏区域并向外扩张，将数据z轴的spacing调整到1mm。

将3D nii格式的数据切片为2D png格式。

# 将灰度值在阈值之外的截断掉# upper = 200# lower = -200ct_array[ct_array > para.upper] = para.upperct_array[ct_array < para.lower] = para.lower#肝脏分割设置seg_array[seg_array > 0] = 1# 找到肝脏区域开始和结束的slice，并各向外扩张z = np.any(seg_array, axis=(1, 2))start_slice, end_slice = np.where(z)[0][[0, -1]]# 对CT数据在横断面上进行降采样(下采样),并进行重采样,将所有数据的z轴的spacing调整到1mm ct_array = ndimage.zoom(ct_array,(ct.GetSpacing()[-1] / para.slice_thickness, para.down_scale, para.down_scale),order=3) seg_array = ndimage.zoom(seg_array, (ct.GetSpacing()[-1] / para.slice_thickness, para.down_scale, para.down_scale), order=0)#进行切片操作for i in range(volume.shape[0]):slice = volume[i,:,:]slice_post = WL(slice, WC, WW)slice_post = slice_post.astype(np.uint8)# slice_post = cv.equalizeHist(slice_post)slices_in_order.append(slice_post)

                                            原始CT                                            调窗后

二、图像增强

同时对肝脏的 Original CT,Ground Truth进行图像增强。

# 图像旋转： 按照概率0.8执行，最大左旋角度10，最大右旋角度10 # p.rotate(probability=0.5, max_left_rotation=20, max_right_rotation=20)# 图像左右互换： 按照概率0.5执行 # p.flip_left_right(probability=0.8)# # # 图像放大缩小： 按照概率0.8执行，面积为原始图0.85倍 # p.zoom_random(probability=1, percentage_area=0.8)

 

三、训练

1.模型搭建

import torch.nn as nn import torch from torch import autogradclass DoubleConv(nn.Module):def __init__(self, in_ch, out_ch):super(DoubleConv, self).__init__()self.conv = nn.Sequential(nn.Conv2d(in_ch, out_ch, 3, padding=1),nn.BatchNorm2d(out_ch),nn.ReLU(inplace=True),nn.Conv2d(out_ch, out_ch, 3, padding=1),nn.BatchNorm2d(out_ch),nn.ReLU(inplace=True))def forward(self, input):return self.conv(input)class UNet(nn.Module):def __init__(self, in_ch, out_ch):super(UNet, self).__init__()self.conv1 = DoubleConv(in_ch, 64)self.pool1 = nn.MaxPool2d(2)self.conv2 = DoubleConv(64, 128)self.pool2 = nn.MaxPool2d(2)self.conv3 = DoubleConv(128, 256)self.pool3 = nn.MaxPool2d(2)self.conv4 = DoubleConv(256, 512)self.pool4 = nn.MaxPool2d(2)self.conv5 = DoubleConv(512, 1024)self.up6 = nn.ConvTranspose2d(1024, 512, 2, stride=2)self.conv6 = DoubleConv(1024, 512)self.up7 = nn.ConvTranspose2d(512, 256, 2, stride=2)self.conv7 = DoubleConv(512, 256)self.up8 = nn.ConvTranspose2d(256, 128, 2, stride=2)self.conv8 = DoubleConv(256, 128)self.up9 = nn.ConvTranspose2d(128, 64, 2, stride=2)self.conv9 = DoubleConv(128, 64)self.conv10 = nn.Conv2d(64, out_ch, 1)def forward(self, x):c1 = self.conv1(x)p1 = self.pool1(c1)c2 = self.conv2(p1)p2 = self.pool2(c2)c3 = self.conv3(p2)p3 = self.pool3(c3)c4 = self.conv4(p3)p4 = self.pool4(c4)c5 = self.conv5(p4)up_6 = self.up6(c5)merge6 = torch.cat([up_6, c4], dim=1)c6 = self.conv6(merge6)up_7 = self.up7(c6)merge7 = torch.cat([up_7, c3], dim=1)c7 = self.conv7(merge7)up_8 = self.up8(c7)merge8 = torch.cat([up_8, c2], dim=1)c8 = self.conv8(merge8)up_9 = self.up9(c8)merge9 = torch.cat([up_9, c1], dim=1)c9 = self.conv9(merge9)c10 = self.conv10(c9)out = nn.Sigmoid()(c10)return out

2.模型训练

if __name__ == '__main__':device = torch.device("cuda" if torch.cuda.is_available() else "cpu")LEARNING_RATE = 1e-3LR_DECAY_STEP = 2LR_DECAY_FACTOR = 0.5WEIGHT_DECAY = 5e-4BATCH_SIZE = 1MAX_EPOCHS = 2model = UNet()criterion = DiceLoss()if opt.use_gpu:criterion = criterion.cuda(opt.device)lr = opt.lroptimizer = torch.optim.Adam(model.parameters(), lr=lr, weight_decay=opt.weight_decay)lr_scheduler = torch.optim.lr_scheduler.ExponentialLR(optimizer, opt.lr_decay)MODEL = Unet(out_ch=2)OPTIMIZER = torch.optim.Adam(MODEL.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)LR_SCHEDULER = torch.optim.lr_scheduler.StepLR(OPTIMIZER, step_size=LR_DECAY_STEP, gamma=LR_DECAY_FACTOR)CRITERION = DiceLoss().to(device)

四、测试

for i in range(1, 10):idx_list.append(i)ct = sitk.ReadImage(data_path + 'volume-' + str(i) + '.nii',sitk.sitkInt16)seg = sitk.ReadImage(data_path + 'segmentation-' + str(i) + '.nii',sitk.sitkInt16)predictions_in_order = []for slice in slices_in_order:slice = torch.from_numpy(slice).float() / 255.output = model(slice.unsqueeze(0).unsqueeze(0))prediction = sm(output)_, prediction = torch.max(prediction, dim=1) #返回每一行中最大值的那个元素，且返回其索引（返回最大元素在这一行的列索引）prediction = prediction.squeeze(0).cpu().detach().numpy().astype(np.uint8)predictions_in_order.append(prediction)

测试结果可视化：

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总结

第一次写，也是随手一写，后续会更新3D肝脏分割，2.5D肝脏分割，以及肝脏肿瘤分割。

总结

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