给出通过搭建UNet网络完成基于深度卷积网络的肝脏分割的实现代码，读取数据集的方式为在一个文件夹内读取多个.nii文件的.gz文件压缩包，并使用标准的DICE系数对分割结果进行评估，且需要显示肝脏的最终分割效果图

好的，以下是基于深度卷积网络的肝脏分割的实现代码：

import os
import gzip
import numpy as np
import nibabel as nib
import tensorflow as tf
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv3D, MaxPooling3D, UpSampling3D, concatenate
from tensorflow.keras.optimizers import Adam
from sklearn.model_selection import train_test_split

# 读取数据集
def load_data(data_path):
    image_list = []
    label_list = []
    for file in os.listdir(data_path):
        if file.endswith('.gz'):
            data = nib.load(os.path.join(data_path, file)).get_fdata()
            if 'image' in file:
                image_list.append(data)
            elif 'label' in file:
                label_list.append(data)
    return np.array(image_list), np.array(label_list)

# 构建UNet网络
def unet3d(input_size=(256, 256, 256, 1)):
    inputs = Input(input_size)

    # 编码器
    conv1 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu', padding='same')(inputs)
    conv1 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv1)
    pool1 = MaxPooling3D(pool_size=(2, 2, 2))(conv1)

    conv2 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu', padding='same')(pool1)
    conv2 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv2)
    pool2 = MaxPooling3D(pool_size=(2, 2, 2))(conv2)

    conv3 = Conv3D(filters=128, kernel_size=(3, 3, 3), activation='relu', padding='same')(pool2)
    conv3 = Conv3D(filters=128, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv3)
    pool3 = MaxPooling3D(pool_size=(2, 2, 2))(conv3)

    conv4 = Conv3D(filters=256, kernel_size=(3, 3, 3), activation='relu', padding='same')(pool3)
    conv4 = Conv3D(filters=256, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv4)

    # 解码器
    up1 = UpSampling3D(size=(2, 2, 2))(conv4)
    up1 = concatenate([conv3, up1], axis=-1)
    conv5 = Conv3D(filters=128, kernel_size=(3, 3, 3), activation='relu', padding='same')(up1)
    conv5 = Conv3D(filters=128, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv5)

    up2 = UpSampling3D(size=(2, 2, 2))(conv5)
    up2 = concatenate([conv2, up2], axis=-1)
    conv6 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu', padding='same')(up2)
    conv6 = Conv3D(filters=64, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv6)

    up3 = UpSampling3D(size=(2, 2, 2))(conv6)
    up3 = concatenate([conv1, up3], axis=-1)
    conv7 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu', padding='same')(up3)
    conv7 = Conv3D(filters=32, kernel_size=(3, 3, 3), activation='relu', padding='same')(conv7)

    outputs = Conv3D(filters=1, kernel_size=(1, 1, 1), activation='sigmoid')(conv7)

    model = Model(inputs=inputs, outputs=outputs)
    model.compile(optimizer=Adam(lr=1e-4), loss='binary_crossentropy', metrics=['accuracy'])
    return model

# 训练模型
def train_model(model, X_train, Y_train, X_val, Y_val, epochs=50, batch_size=1):
    model.fit(X_train, Y_train, validation_data=(X_val, Y_val), epochs=epochs, batch_size=batch_size)

# 预测
def predict(model, X_test):
    Y_pred = model.predict(X_test)
    return Y_pred

# 计算DICE系数
def dice_coef(y_true, y_pred):
    y_true_f = tf.keras.backend.flatten(y_true)
    y_pred_f = tf.keras.backend.flatten(y_pred)
    intersection = tf.keras.backend.sum(y_true_f * y_pred_f)
    return (2. * intersection + 1) / (tf.keras.backend.sum(y_true_f) + tf.keras.backend.sum(y_pred_f) + 1)

# 加载数据集
data_path = '/path/to/dataset'
X, Y = load_data(data_path)

# 数据预处理
X = np.expand_dims(X, axis=-1)
Y = np.expand_dims(Y, axis=-1)

# 划分训练集和测试集
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)

# 构建模型
model = unet3d(input_size=(256, 256, 256, 1))

# 训练模型
train_model(model, X_train, Y_train, X_test, Y_test)

# 预测结果
Y_pred = predict(model, X_test)

# 计算DICE系数
dice = dice_coef(Y_test, Y_pred)
print('DICE coefficient:', dice)

# 显示肝脏的最终分割效果图
import matplotlib.pyplot as plt
plt.imshow(Y_pred[0,:,:,:,0], cmap='gray')
plt.show()

注意事项：

1. 代码中的`/path/to/dataset`需要替换为实际数据集所在的路径。
2. 由于数据集可能非常大，因此建议在训练模型时使用GPU加速。
3. 在计算DICE系数时，需要使用`tf.keras.backend`进行计算，否则可能会出现维度不匹配的错误。
4. 肝脏的最终分割效果图需要根据实际情况进行调整，这里仅提供一种简单的显示方式。