脑肿瘤分割与分类的人工智能研究报告

第一部分：引言

脑肿瘤按组织来源可分为原发性（如胶质瘤）和继发性（转移性肿瘤）。MRI 提供多序列图像（T1、T1c、T2、FLAIR），为 AI 模型提供丰富数据，但存在形态多样、边界不规则和异质性强等特点。AI 能自动提取特征，实现精确分割与分类，辅助临床决策。

第二部分：数据处理与预处理

2.1 数据加载

假设使用 BraTS 2021 数据集：

复制代码

import nibabel as nib
import numpy as np
import os

def load_brats_image(path):
    """加载 NIfTI MRI 图像"""
    img = nib.load(path)
    return img.get_fdata()

# 示例
t1_path = 'BraTS2021/train/BRATS_001/T1.nii.gz'
t1_img = load_brats_image(t1_path)
print("Image shape:", t1_img.shape)

2.2 数据增强

复制代码

from tensorflow.keras.preprocessing.image import ImageDataGenerator

datagen = ImageDataGenerator(
    rotation_range=15,
    width_shift_range=0.1,
    height_shift_range=0.1,
    zoom_range=0.1,
    horizontal_flip=True
)

# 对二维切片应用增强
augmented = datagen.flow(np.expand_dims(t1_img[:,:,50], axis=(0,-1)), batch_size=1)

2.3 标签处理

复制代码

def load_label(path):
    label = nib.load(path).get_fdata()
    # 将不同肿瘤区域编码为 0,1,2,3
    label[label==4] = 3
    return label

第三部分：脑肿瘤分割模型（U-Net 3D）

复制代码

import tensorflow as tf
from tensorflow.keras import layers, models

def build_3d_unet(input_shape=(128,128,128,1), num_classes=4):
    inputs = layers.Input(shape=input_shape)
    # 编码器
    c1 = layers.Conv3D(32, (3,3,3), activation='relu', padding='same')(inputs)
    c1 = layers.Conv3D(32, (3,3,3), activation='relu', padding='same')(c1)
    p1 = layers.MaxPooling3D((2,2,2))(c1)
    
    c2 = layers.Conv3D(64, (3,3,3), activation='relu', padding='same')(p1)
    c2 = layers.Conv3D(64, (3,3,3), activation='relu', padding='same')(c2)
    p2 = layers.MaxPooling3D((2,2,2))(c2)
    
    # 底层
    c3 = layers.Conv3D(128, (3,3,3), activation='relu', padding='same')(p2)
    c3 = layers.Conv3D(128, (3,3,3), activation='relu', padding='same')(c3)
    
    # 解码器
    u2 = layers.UpSampling3D((2,2,2))(c3)
    u2 = layers.concatenate([u2, c2])
    c4 = layers.Conv3D(64, (3,3,3), activation='relu', padding='same')(u2)
    
    u1 = layers.UpSampling3D((2,2,2))(c4)
    u1 = layers.concatenate([u1, c1])
    c5 = layers.Conv3D(32, (3,3,3), activation='relu', padding='same')(u1)
    
    outputs = layers.Conv3D(num_classes, (1,1,1), activation='softmax')(c5)
    
    model = models.Model(inputs, outputs)
    return model

model = build_3d_unet()
model.summary()

3.1 训练与评估

复制代码

model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
# X_train, y_train 为训练数据
# model.fit(X_train, y_train, batch_size=2, epochs=50, validation_split=0.2)

第四部分：脑肿瘤分类模型（CNN）

4.1 2D CNN 分类示例

复制代码

from tensorflow.keras import layers, models

def build_2d_cnn(input_shape=(128,128,1), num_classes=3):
    model = models.Sequential([
        layers.Conv2D(32, (3,3), activation='relu', input_shape=input_shape),
        layers.MaxPooling2D((2,2)),
        layers.Conv2D(64, (3,3), activation='relu'),
        layers.MaxPooling2D((2,2)),
        layers.Flatten(),
        layers.Dense(128, activation='relu'),
        layers.Dense(num_classes, activation='softmax')
    ])
    return model

cnn_model = build_2d_cnn()
cnn_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

4.2 多序列融合

复制代码

# 假设 T1、T2、FLAIR 三序列
import tensorflow as tf

input_t1 = layers.Input(shape=(128,128,1))
input_t2 = layers.Input(shape=(128,128,1))
input_flair = layers.Input(shape=(128,128,1))

def conv_block(x):
    x = layers.Conv2D(32, (3,3), activation='relu', padding='same')(x)
    x = layers.MaxPooling2D((2,2))(x)
    return x

x1 = conv_block(input_t1)
x2 = conv_block(input_t2)
x3 = conv_block(input_flair)

merged = layers.concatenate([x1, x2, x3])
flatten = layers.Flatten()(merged)
dense = layers.Dense(128, activation='relu')(flatten)
output = layers.Dense(3, activation='softmax')(dense)

multi_modal_model = tf.keras.Model(inputs=[input_t1, input_t2, input_flair], outputs=output)
multi_modal_model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

第五部分：模型可视化与评价

复制代码

import matplotlib.pyplot as plt

# 可视化分割结果
pred_mask = model.predict(X_val[0:1])[0]
plt.imshow(pred_mask[:,:,64,1])  # 显示第64层的第1类预测
plt.show()

复制代码

评价指标：Dice 系数、IoU、准确率、召回率

BraTS 官方挑战赛指标：Dice 对增强肿瘤（ET）、肿瘤核心（TC）、全肿瘤（WT）

第六部分：前沿技术与挑战

自监督学习：利用未标注 MRI 提升泛化能力
生成模型（GAN）数据增强
联邦学习：跨医院模型训练，保护隐私
3D 高分辨率模型计算成本高
模型可解释性不足，影响临床信任度

第七部分：未来方向

多模态数据融合（影像+临床+基因组）
可解释 AI 和人机协作诊疗
轻量化模型用于实时 MRI 分析
多中心验证和临床落地

参考文献

Menze, B. et al., The Multimodal Brain Tumor Image Segmentation Benchmark (BraTS), IEEE TMI, 2015
Ronneberger, O. et al., U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI, 2015
Kamnitsas, K. et al., Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation, MedIA, 2017
MONAI: Medical Open Network for AI (https://monai.io)
Goodfellow, I., Deep Learning, MIT Press, 2016