基于分割网络Unet生成虚拟图像

本文基于Unet分割网络，利用49例头部磁共振T1、T2数据，通过配准使二者解剖位置一致，转换数据格式并裁剪窗宽窗位，构建数据集。以T1为输入、T2为标签训练Unet进行回归，用SSIM评估，经200轮训练，最佳SSIM达0.571，实现由T1生成虚拟T2图像。

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基于分割网络Unet生成虚拟图像

这个想法是基于

[1]高留刚, 李春迎, 陆正大,等. 基于卷积神经网络生成虚拟平扫CT图像[J]. 中国医学影像技术, 2022, 38(3):5.

【1】文章使用Unet对增强CT数据进行训练，最终预测生成对应的虚拟平扫CT图像，达到只需要对患者扫描一次CT即可，避免患者接受过多的放射辐射。

【2】因为本项目没有对应的CT数据，刚好有一个T1、T2磁共振头部公开数据，先磁共振数据进行配准，然后输入T1模态的数据到Unet中然后生成对应的T2模态数据。

1. 数据

数量：49例

模态和部位：磁共振的T1和T2头部数据 

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#解压数据!unzip -o /home/aistudio/data/data146796/mydata.zip -d /home/aistudio/work

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#antspyx 是一个配准工具包，SImpleITK处理医学数据!pip install antspyx SimpleITK

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import osimport antsimport SimpleITK as sitkfrom tqdm import tqdmimport numpy as npimport randomimport matplotlib.pyplot as pltimport paddleimport cv2

2. 对数据进行配准

项是利用 Unet对像素数值进行回归预测。所以在进入网路之前，除了像素值的尺寸不同之外，原资料与标记资料的解剖学位置应当是相同的。因此，必须将原始资料与标签资料进行匹配，使二者重新整合。比如下面这张图片，T1有256层，T2有136层，显然是解剖位置不对，然后经过配准后的T2有256层，和T1层的解剖学位置一模一样。对齐后的数据可以作为标记输入到网络中。

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#因为配准花费的时间太久太久了，本项目的数据已经包含好了配准后的数据。#因此不用运行此代码path  = '/home/aistudio/work/mydata/IXI-T1'for f in tqdm(os.listdir(path)):    f_path = os.path.join(path,f)    m_path = os.path.join('/home/aistudio/work/mydata/IXI-T2',f.replace('T1','T2'))    f_img = ants.image_read(f_path)    m_img = ants.image_read(m_path)    mytx = ants.registration(fixed=f_img, moving=m_img, type_of_transform='SyN')    # 将形变场作用于moving图像，得到配准后的图像    warped_img = ants.apply_transforms(fixed=f_img, moving=m_img, transformlist=mytx['fwdtransforms'],                                    interpolator="linear")    # 将配准后图像的direction/origin/spacing和原图保持一致    warped_img.set_direction(f_img.direction)    warped_img.set_origin(f_img.origin)    warped_img.set_spacing(f_img.spacing)    img_name = '/home/aistudio/work/mydata/IXI-T2-warped/'+ f.replace('T1','Warped')    # ants.image_write(warped_img, img_name)print("End")

3. 转换数据形式

原始数据格式是NIFIT，是属于医疗数据格式的一种。把NIFIT数据转换成numpy,并对窗宽窗位进行裁剪到0~255。并生成txt文档，用作构建DataSet使用。

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random.seed(2022)save_path = '/home/aistudio/work/npdata'save_origin_path = "/home/aistudio/work/npdata/origin/"save_label_path =  "/home/aistudio/work/npdata/label/"if not os.path.exists(save_path):    os.mkdir(save_path)    os.mkdir(save_origin_path)    os.mkdir(save_label_path)origin_data = '/home/aistudio/work/mydata/IXI-T1'f_list = os.listdir(origin_data)random.shuffle(f_list)split = int(len(f_list)*0.9)t_f_list = f_list[:split]v_f_list = f_list[split:]def gen_txt(f_list,name):    txt = open( os.path.join(save_path,name),'w')    for f in f_list:        if '.ipynb_checkpoints' in f:            continue        f_path = os.path.join(origin_data,f)        tag_path = os.path.join(origin_data.replace('T1','T2-warped'),f.replace('T1','Warped'))        f_sitkData = sitk.ReadImage(f_path)        tag_sitkData = sitk.ReadImage(tag_path)        f_npData = sitk.GetArrayFromImage(f_sitkData)        tag_npData = sitk.GetArrayFromImage(tag_sitkData)        f.split('-')[0]        for i in range(100,200):            f_slice = np.rot90(f_npData[:,i,:],-1)            tag_slice = np.rot90(tag_npData[:,i,:],-1)            f_slice = (f_slice - 0) / ((835 - 0) / 255)            np.clip(f_slice, 0, 255, out=f_slice)            tag_slice = (tag_slice - 0) / ((626 - 0) / 255)            np.clip(tag_slice, 0, 255, out=tag_slice)                        f_save_path = save_origin_path+f.split('-')[0]+'_'+str(i) +'.npy'            tag_save_path = save_label_path+f.split('-')[0]+'_'+str(i) +'.npy'            np.save(f_save_path,f_slice)            np.save(tag_save_path,tag_slice)            txt.write(f_save_path + ' ' + tag_save_path+ 'n')    txt.close()gen_txt(t_f_list,name='train.txt')gen_txt(v_f_list,name='val.txt')print('完成')

4. 构建Dataset

数据增强只采用 缩放到256×256。

In [4]

from paddle.io import Dataset, DataLoaderfrom paddle.vision.transforms import Resizeclass MyDataset(Dataset):    def __init__(self, data_dir, txt_path, transform=None):        super(MyDataset, self).__init__()        self.data_list = []        with open(txt_path,encoding='utf-8') as f:            for line in f.readlines():                image_path, label_path = line.split(' ')                image_path = image_path.strip()                label_path = label_path.strip()                image_path = os.path.join(data_dir, image_path)                label_path = os.path.join(data_dir,label_path)                self.data_list.append([image_path, label_path])        self.transform = transform    def __getitem__(self, index):        image_path, label_path = self.data_list[index]        image = np.load(image_path)        label = np.load(label_path)        if self.transform is not None:            image = self.transform(image)            label = self.transform(label)                image = image[np.newaxis,:].astype('float32')        label = label[np.newaxis,:].astype('float32')        return image, label    def __len__(self):        return len(self.data_list)BatchSize = 24transform = Resize(size=(256,256))t_dataset = MyDataset('/home/aistudio','work/npdata/train.txt',transform=transform)v_dataset = MyDataset('/home/aistudio','work/npdata/val.txt',transform=transform)train_loader = DataLoader(t_dataset,batch_size=BatchSize)  val_loader = DataLoader(v_dataset,batch_size=BatchSize)

5. 评价指标 SSIM

SSIM (Structure Similarity Index Measure) 结构衡量指标

结构相似指标可以衡量图片的失真程度，也可以衡量两张图片的相似程度，SSIM指标感知模型，即更符合人眼的直观感受。SSIM 主要考量图片的三个关键特征：亮度（Luminance）, 对比度（Contrast）, 结构 (Structure)。SSIM取值约接近1，两张图片的相似度约接近。两张图片一模一样，SSIM=1

参考文章 https://zhuanlan.zhihu.com/p/399215180

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def ssim(img1, img2):  C1 = (0.01 * 255)**2  C2 = (0.03 * 255)**2  img1 = img1.astype(np.float64)  img2 = img2.astype(np.float64)  kernel = cv2.getGaussianKernel(11, 1.5)  window = np.outer(kernel, kernel.transpose())  mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid  mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]  mu1_sq = mu1**2  mu2_sq = mu2**2  mu1_mu2 = mu1 * mu2  sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq  sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq  sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2  ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *                              (sigma1_sq + sigma2_sq + C2))  return ssim_map.mean()def calculate_ssim(img1, img2):  if not img1.shape == img2.shape:    raise ValueError('Input images must have the same dimensions.')  if img1.ndim == 2:    return ssim(img1, img2)  elif img1.ndim == 3:    if img1.shape[2] == 3:      ssims = []      for i in range(3):        ssims.append(ssim(img1, img2))      return np.array(ssims).mean()    elif img1.shape[2] == 1:      return ssim(np.squeeze(img1), np.squeeze(img2))  else:    raise ValueError('Wrong input image dimensions.')for data in t_dataset:    img1,img2 = data    plt.figure(figsize=(10,6))    plt.subplot(1,2,1)    plt.imshow(np.squeeze(img1),'gray')    plt.title("T1 mode")    plt.subplot(1,2,2)    plt.imshow(np.squeeze(img2),'gray')    plt.title("T2 mode")    print(f'SSIM:{calculate_ssim(np.squeeze(img1),np.squeeze(img2))}')    break

SSIM:0.4927141870957079

6. 导入Unet模型，开始训练

Unet网络是一种语义分割网络，输入原始图像和对应的标签Mask图像到Unet网络中进行分割，最终输出和标签Mask一致的图像。实际也是对像素值进行分类。把原始图像中的像素值进行分类，到底是属于Mask图像中像素值0，还是像素值1等等。这里是使用Unet网络进行回归，因此网络输出的类别是1。

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from model.unet import UNetimg = paddle.rand([2, 1, 256, 256])model = UNet()out = model(paddle.rand([2, 1, 256, 256]))print(out.shape)

[2, 1, 256, 256]

In [9]

def evaluation(model,val_dataset):    """验证"""    model.eval()    ssims = []    for data in val_dataset:        image,label = data        image = image[np.newaxis,:].astype('float32')        image = paddle.to_tensor(image)        pre = model(image).numpy()        pre = np.squeeze(pre).astype('int32')        ssims.append(calculate_ssim(np.squeeze(label.astype('int32')),pre))    return np.array(ssims).mean()def train(model,epochs):    if not os.path.exists('/home/aistudio/save_model'):        os.mkdir('/home/aistudio/save_model')    steps = int(len(t_dataset)/BatchSize) * epochs    best_ssim = 0 #记录最优的ssim得分    model.train()    scheduler = paddle.optimizer.lr.PolynomialDecay(learning_rate=0.05, decay_steps=steps, verbose=False)    opt = paddle.optimizer.Adam(learning_rate=scheduler, parameters=model.parameters())        for epoch_id in range(epochs):        for batch_id, data in enumerate(train_loader()):            images, labels = data            images = paddle.to_tensor(images)            labels = paddle.to_tensor(labels)            predicts = model(images)            mse_loss = paddle.nn.MSELoss()            l1_loss = paddle.nn.L1Loss()            loss = mse_loss(predicts, labels)+l1_loss(predicts,labels)            if batch_id % 200 == 0:                print("epoch: {}, batch: {}, loss is: {}".format(epoch_id, batch_id, loss.numpy()))            loss.backward()            opt.step()            opt.clear_grad()        #训练过程中，保存模型参数        if epoch_id % 10 == 0:            paddle.save(model.state_dict(), '/home/aistudio/save_model/'+str(epoch_id)+'model.pdparams')                        model.eval()            #训练过程中，预览效果，输入T1图片，预测t2图片            data = t_dataset[150]            img,label = data            input = img[np.newaxis,:].astype('float32')            input = paddle.to_tensor(input)            pre = model(input).numpy()            pre = np.squeeze(pre)            plt.figure(figsize=(12,6))            plt.subplot(1,3,1)            plt.imshow(np.squeeze(img),'gray')            plt.title("input: T1 mode")            plt.subplot(1,3,2)            plt.imshow(np.squeeze(label),'gray')            plt.title("target:T2 mode")            plt.subplot(1,3,3)            plt.imshow(pre,'gray')            plt.title("pre: T2 mode")            plt.show()            v_ssim_mean = evaluation(model, v_dataset)            if v_ssim_mean > best_ssim:                paddle.save(model.state_dict(), '/home/aistudio/save_model/best_model.pdparams')                best_ssim = v_ssim_mean            model.train()            print(f'val_SSIM: {v_ssim_mean},Best_SSIM:{best_ssim}')       #启动训练过程train(model,epochs= 200)

epoch: 0, batch: 0, loss is: [7316.7188]

val_SSIM: 0.39978080604275146,Best_SSIM:0.39978080604275146epoch: 1, batch: 0, loss is: [2628.9192]epoch: 2, batch: 0, loss is: [2724.6853]epoch: 3, batch: 0, loss is: [2688.8508]epoch: 4, batch: 0, loss is: [2686.88]epoch: 5, batch: 0, loss is: [2608.186]epoch: 6, batch: 0, loss is: [2520.5952]epoch: 7, batch: 0, loss is: [2692.3694]epoch: 8, batch: 0, loss is: [2702.1028]epoch: 9, batch: 0, loss is: [2628.6016]epoch: 10, batch: 0, loss is: [2485.4382]

val_SSIM: 0.4802982366278921,Best_SSIM:0.4802982366278921epoch: 11, batch: 0, loss is: [2507.683]epoch: 12, batch: 0, loss is: [2434.3452]epoch: 13, batch: 0, loss is: [2408.262]epoch: 14, batch: 0, loss is: [2332.7097]epoch: 15, batch: 0, loss is: [2270.0247]epoch: 16, batch: 0, loss is: [2179.795]epoch: 17, batch: 0, loss is: [2138.3933]epoch: 18, batch: 0, loss is: [2093.4106]epoch: 19, batch: 0, loss is: [1887.1506]epoch: 20, batch: 0, loss is: [2024.5569]

val_SSIM: 0.49954443953370115,Best_SSIM:0.49954443953370115epoch: 21, batch: 0, loss is: [1973.8619]epoch: 22, batch: 0, loss is: [1956.6097]epoch: 23, batch: 0, loss is: [1909.3981]epoch: 24, batch: 0, loss is: [1875.7285]epoch: 25, batch: 0, loss is: [1837.553]epoch: 26, batch: 0, loss is: [1812.5168]epoch: 27, batch: 0, loss is: [1785.5492]epoch: 28, batch: 0, loss is: [1774.7295]epoch: 29, batch: 0, loss is: [1752.758]epoch: 30, batch: 0, loss is: [1756.8203]

val_SSIM: 0.5426780015544808,Best_SSIM:0.5426780015544808epoch: 31, batch: 0, loss is: [1735.7894]epoch: 32, batch: 0, loss is: [1827.0814]epoch: 33, batch: 0, loss is: [1753.9219]epoch: 34, batch: 0, loss is: [1779.7365]epoch: 35, batch: 0, loss is: [1752.4994]epoch: 36, batch: 0, loss is: [1747.4668]epoch: 37, batch: 0, loss is: [1735.7844]epoch: 38, batch: 0, loss is: [1769.8733]epoch: 39, batch: 0, loss is: [1759.1631]epoch: 40, batch: 0, loss is: [1630.8364]

val_SSIM: 0.4373114577520071,Best_SSIM:0.5426780015544808epoch: 41, batch: 0, loss is: [1611.3628]epoch: 42, batch: 0, loss is: [1715.6615]epoch: 43, batch: 0, loss is: [1763.4269]epoch: 44, batch: 0, loss is: [1737.9359]epoch: 45, batch: 0, loss is: [1772.5739]epoch: 46, batch: 0, loss is: [1738.1847]epoch: 47, batch: 0, loss is: [1775.006]epoch: 48, batch: 0, loss is: [1911.8531]epoch: 49, batch: 0, loss is: [2033.4453]epoch: 50, batch: 0, loss is: [1987.3456]

val_SSIM: 0.5579299673856584,Best_SSIM:0.5579299673856584epoch: 51, batch: 0, loss is: [1836.5175]epoch: 52, batch: 0, loss is: [1861.1411]epoch: 53, batch: 0, loss is: [1845.1718]epoch: 54, batch: 0, loss is: [1740.5963]epoch: 55, batch: 0, loss is: [1660.3125]epoch: 56, batch: 0, loss is: [1726.9702]epoch: 57, batch: 0, loss is: [1635.8535]epoch: 58, batch: 0, loss is: [1610.6799]epoch: 59, batch: 0, loss is: [1568.5394]epoch: 60, batch: 0, loss is: [1617.876]

val_SSIM: 0.5634165131362056,Best_SSIM:0.5634165131362056epoch: 61, batch: 0, loss is: [1561.1827]epoch: 62, batch: 0, loss is: [1597.7261]epoch: 63, batch: 0, loss is: [1525.599]epoch: 64, batch: 0, loss is: [1548.1874]epoch: 65, batch: 0, loss is: [1488.4364]epoch: 66, batch: 0, loss is: [1513.0366]epoch: 67, batch: 0, loss is: [1505.5546]epoch: 68, batch: 0, loss is: [1516.5098]epoch: 69, batch: 0, loss is: [1467.4851]epoch: 70, batch: 0, loss is: [1585.8317]

val_SSIM: 0.5709971328311436,Best_SSIM:0.5709971328311436epoch: 71, batch: 0, loss is: [1457.6887]epoch: 72, batch: 0, loss is: [1467.6174]epoch: 73, batch: 0, loss is: [1471.8882]epoch: 74, batch: 0, loss is: [1459.0536]epoch: 75, batch: 0, loss is: [1458.9404]epoch: 76, batch: 0, loss is: [1634.24]epoch: 77, batch: 0, loss is: [1650.2292]epoch: 78, batch: 0, loss is: [1437.5302]epoch: 79, batch: 0, loss is: [1421.7593]epoch: 80, batch: 0, loss is: [1413.1609]

val_SSIM: 0.5701560896501561,Best_SSIM:0.5709971328311436epoch: 81, batch: 0, loss is: [1409.3071]epoch: 82, batch: 0, loss is: [1404.0553]epoch: 83, batch: 0, loss is: [1420.5449]epoch: 84, batch: 0, loss is: [1398.2864]epoch: 85, batch: 0, loss is: [1488.5781]epoch: 86, batch: 0, loss is: [1401.356]epoch: 87, batch: 0, loss is: [1410.7175]epoch: 88, batch: 0, loss is: [1500.6272]epoch: 89, batch: 0, loss is: [1416.3497]epoch: 90, batch: 0, loss is: [1380.4263]

val_SSIM: 0.5465621052241267,Best_SSIM:0.5709971328311436epoch: 91, batch: 0, loss is: [1372.7605]epoch: 92, batch: 0, loss is: [1367.5848]epoch: 93, batch: 0, loss is: [1376.7378]epoch: 94, batch: 0, loss is: [1370.2089]epoch: 95, batch: 0, loss is: [1365.8433]epoch: 96, batch: 0, loss is: [1369.1512]epoch: 97, batch: 0, loss is: [1369.3212]epoch: 98, batch: 0, loss is: [1404.3115]epoch: 99, batch: 0, loss is: [1354.4286]epoch: 100, batch: 0, loss is: [1382.9532]

val_SSIM: 0.5378666148453135,Best_SSIM:0.5709971328311436epoch: 101, batch: 0, loss is: [1387.7471]epoch: 102, batch: 0, loss is: [1424.3396]epoch: 103, batch: 0, loss is: [1349.6024]epoch: 104, batch: 0, loss is: [1366.5181]epoch: 105, batch: 0, loss is: [1352.3512]epoch: 106, batch: 0, loss is: [1353.1865]epoch: 107, batch: 0, loss is: [1352.0631]epoch: 108, batch: 0, loss is: [1343.2163]epoch: 109, batch: 0, loss is: [1345.3413]epoch: 110, batch: 0, loss is: [1346.6847]

val_SSIM: 0.5586893791021966,Best_SSIM:0.5709971328311436epoch: 111, batch: 0, loss is: [1343.9454]epoch: 112, batch: 0, loss is: [1349.0774]epoch: 113, batch: 0, loss is: [1337.5864]epoch: 114, batch: 0, loss is: [1429.8538]epoch: 115, batch: 0, loss is: [1349.8104]epoch: 116, batch: 0, loss is: [1335.1368]epoch: 117, batch: 0, loss is: [1337.9221]epoch: 118, batch: 0, loss is: [1352.1951]epoch: 119, batch: 0, loss is: [1347.4542]epoch: 120, batch: 0, loss is: [1336.236]

val_SSIM: 0.4977443843057142,Best_SSIM:0.5709971328311436epoch: 121, batch: 0, loss is: [1341.1677]epoch: 122, batch: 0, loss is: [1325.7648]epoch: 123, batch: 0, loss is: [1322.5]epoch: 124, batch: 0, loss is: [1322.8683]epoch: 125, batch: 0, loss is: [1319.6675]epoch: 126, batch: 0, loss is: [1319.3303]epoch: 127, batch: 0, loss is: [1320.0753]epoch: 128, batch: 0, loss is: [1316.222]epoch: 129, batch: 0, loss is: [1336.7915]epoch: 130, batch: 0, loss is: [1321.2736]

val_SSIM: 0.47030362428033146,Best_SSIM:0.5709971328311436epoch: 131, batch: 0, loss is: [1339.2891]epoch: 132, batch: 0, loss is: [1318.2183]epoch: 133, batch: 0, loss is: [1325.5737]epoch: 134, batch: 0, loss is: [1308.886]epoch: 135, batch: 0, loss is: [1298.8752]epoch: 136, batch: 0, loss is: [1302.0266]epoch: 137, batch: 0, loss is: [1279.0824]epoch: 138, batch: 0, loss is: [1313.5026]epoch: 139, batch: 0, loss is: [1317.8335]epoch: 140, batch: 0, loss is: [1325.1211]

val_SSIM: 0.5129117285623543,Best_SSIM:0.5709971328311436epoch: 141, batch: 0, loss is: [1307.1959]epoch: 142, batch: 0, loss is: [1312.2686]epoch: 143, batch: 0, loss is: [1320.1404]epoch: 144, batch: 0, loss is: [1326.3783]epoch: 145, batch: 0, loss is: [1308.4757]epoch: 146, batch: 0, loss is: [1318.8088]epoch: 147, batch: 0, loss is: [1297.8518]epoch: 148, batch: 0, loss is: [1296.301]epoch: 149, batch: 0, loss is: [1301.2318]epoch: 150, batch: 0, loss is: [1290.0143]

val_SSIM: 0.4650640222375466,Best_SSIM:0.5709971328311436epoch: 151, batch: 0, loss is: [1320.3531]epoch: 152, batch: 0, loss is: [1311.9688]epoch: 153, batch: 0, loss is: [1302.6729]epoch: 154, batch: 0, loss is: [1302.4635]epoch: 155, batch: 0, loss is: [1286.9762]epoch: 156, batch: 0, loss is: [1284.86]epoch: 157, batch: 0, loss is: [1290.937]epoch: 158, batch: 0, loss is: [1274.3252]epoch: 159, batch: 0, loss is: [1280.6571]epoch: 160, batch: 0, loss is: [1286.4685]

val_SSIM: 0.4561319579342975,Best_SSIM:0.5709971328311436epoch: 161, batch: 0, loss is: [1270.9604]epoch: 162, batch: 0, loss is: [1287.5771]epoch: 163, batch: 0, loss is: [1269.8563]epoch: 164, batch: 0, loss is: [1286.9226]epoch: 165, batch: 0, loss is: [1272.0933]epoch: 166, batch: 0, loss is: [1274.8392]epoch: 167, batch: 0, loss is: [1272.4178]epoch: 168, batch: 0, loss is: [1266.7671]epoch: 169, batch: 0, loss is: [1282.4298]epoch: 170, batch: 0, loss is: [1247.015]

val_SSIM: 0.49646587443554935,Best_SSIM:0.5709971328311436epoch: 171, batch: 0, loss is: [1250.8077]epoch: 172, batch: 0, loss is: [1287.3835]epoch: 173, batch: 0, loss is: [1248.3782]epoch: 174, batch: 0, loss is: [1249.7322]epoch: 175, batch: 0, loss is: [1273.4828]epoch: 176, batch: 0, loss is: [1253.0632]epoch: 177, batch: 0, loss is: [1281.6831]epoch: 178, batch: 0, loss is: [1280.6669]epoch: 179, batch: 0, loss is: [1245.0092]epoch: 180, batch: 0, loss is: [1257.4398]

val_SSIM: 0.46512517332255776,Best_SSIM:0.5709971328311436epoch: 181, batch: 0, loss is: [1242.4584]epoch: 182, batch: 0, loss is: [1254.164]epoch: 183, batch: 0, loss is: [1238.1926]epoch: 184, batch: 0, loss is: [1243.1198]epoch: 185, batch: 0, loss is: [1248.0677]epoch: 186, batch: 0, loss is: [1240.6064]epoch: 187, batch: 0, loss is: [1252.3843]epoch: 188, batch: 0, loss is: [1230.179]epoch: 189, batch: 0, loss is: [1237.1627]epoch: 190, batch: 0, loss is: [1232.43]

val_SSIM: 0.4939679476601408,Best_SSIM:0.5709971328311436epoch: 191, batch: 0, loss is: [1245.5471]epoch: 192, batch: 0, loss is: [1255.1567]epoch: 193, batch: 0, loss is: [1250.8704]epoch: 194, batch: 0, loss is: [1223.4425]epoch: 195, batch: 0, loss is: [1224.8658]epoch: 196, batch: 0, loss is: [1230.7142]epoch: 197, batch: 0, loss is: [1229.7573]epoch: 198, batch: 0, loss is: [1235.9465]epoch: 199, batch: 0, loss is: [1225.0635]

以上就是基于分割网络Unet生成虚拟图像的详细内容，更多请关注创想鸟其它相关文章！