天池医疗AI大赛[第一季]：肺部结节智能诊断

天池医疗AI大赛[第一季]：肺部结节智能诊断

2D卷积神经网络

见Tumor.ipynb

Feature Engineering

坐标变换

pos = np.round((pos - origin) / spacing)
diameter = tumor['diameter_mm'] / spacing[0]

对图像和tumor diameter进行空间坐标变化

Normalization

mean, std = np.mean(img), np.std(img)
img = (img - mean) / std

黑白(0/1)转化

kmeans = KMeans(n_clusters=2).fit(np.reshape(mask, (np.prod(mask.shape), 1)))
threshold = np.mean(kmeans.cluster_centers_)
mask = np.where(mask>=threshold, 1.0, 0.0)

通过Kmeans把image划分为2个cluster，然后找到centroid均值，做划分

dilation & erosion

mask = morphology.dilation(mask, np.ones([4, 4]))
mask = morphology.erosion(mask, np.ones([24, 24]))

这里4和24是通过不断尝试找到的合理值。

Label Region

labeled_mask = measure.label(mask, background=1)
regions = measure.regionprops(labeled_mask)

得到连续的肺的mask，排除身体周围等其他连续区域mask

聚焦

把图像放大到肺部，并保持图像size不变

U-Net神经网络

定义U-Net神经网络

inputs = Input((1, img_rows, img_cols))
conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(inputs)
#       Conv2D(32, (3, 3), activation="relu", padding="same")

conv1 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)

conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(pool1)
conv2 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)

conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(pool2)
conv3 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)

conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(pool3)
conv4 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(conv4)

conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(pool4)
conv5 = Conv2D(512, (3, 3), activation='relu', padding='same')(conv5)

up6 = concatenate([UpSampling2D(size=(2, 2))(conv5), conv4], axis=1)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(up6)
conv6 = Conv2D(256, (3, 3), activation='relu', padding='same')(conv6)

up7 = concatenate([UpSampling2D(size=(2, 2))(conv6), conv3], axis=1)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(up7)
conv7 = Conv2D(128, (3, 3), activation='relu', padding='same')(conv7)

up8 = concatenate([UpSampling2D(size=(2, 2))(conv7), conv2], axis=1)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(up8)
conv8 = Conv2D(64, (3, 3), activation='relu', padding='same')(conv8)

up9 = concatenate([UpSampling2D(size=(2, 2))(conv8), conv1], axis=1)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(up9)
conv9 = Conv2D(32, (3, 3), activation='relu', padding='same')(conv9)

conv10 = Conv2D(1, (1, 1), activation='sigmoid')(conv9)

model = Model(inputs=inputs, outputs=conv10)

model.compile(optimizer=Adam(lr=1.0e-5), loss=dice_coef_loss, metrics=[dice_coef])

确定lost function为y_truth和y_pred的重叠区域*2，除以2个区域的"面积"和。

y_true_f = K.flatten(y_true)
y_pred_f = K.flatten(y_pred)
intersection = K.sum(y_true_f * y_pred_f)
return -(2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

3D卷积神经网络

见Tumor-3D.ipynb

类似于2D卷积神经网络，不同的是，我定义X_train的每一个样本为某个病人的某个tumor位置上下32个slice组成的立体空间block（同样需要做Fine Feature Engineering），y_train为这样一个空间，只有以tumor center point为球心，diameter为直径的一个球体。