junqiangchen / hc18-automated-measurement-of-fetal-head-circumference

HC18—Automated measurement of fetal head circumference

HC18-Automated-measurement-of-fetal-head-circumference

This is an example of the ultrasound imaging is used to measure the fetal head circumference (HC).

Prerequisities

The following dependencies are needed:

• numpy >= 1.11.1
• SimpleITK >=1.0.1
• opencv-python >=3.3.0
• tensorflow-gpu ==1.8.0
• pandas >=0.20.1
• scikit-learn >= 0.17.1

How to Use

1、Preprocess

• download ultrasound image data:https://zenodo.org/record/1327317
• convert annotation image to mask image,you can download here:https://pan.baidu.com/s/1NmmztKR0Omu1fKue7263Sw password：6ax7
• data augmentation:run the augtest.py

2、HC Segmentation

• the VNet2d model

• train and predict in the script of vnet2d_train.py and vnet2d_predict.py

3、trained model can download on here:https://pan.baidu.com/s/1ved3w6Tn4nwnBUDzxW8arw password：r0li

Result

• loss and accuracy,orange line is first trained,blue is second trained

Contact

• https://github.com/junqiangchen
• email: [email protected]
• Contact: junqiangChen
• WeChat Number: 1207173174
• WeChat Public number: 最新医学影像技术

hc18-automated-measurement-of-fetal-head-circumference's Issues

Baidu link broken

Hey,

I am trying to download the masked annotation images from the Baidu link you have given, but I am unable to do so. Can you share a google drive link for the same?

convert annotation image to mask image, you can download here:https://pan.baidu.com/s/1NmmztKR0Omu1fKue7263Sw password：6ax7

This part.

如何进行多标签分类，n_class设置不起作用

请问一下如何进行多标签分类，n_class设置不起作用 ，logits，labels怎么处理以适应多标签分类？

paper

Does the code have a corresponding article?

pytorch版本

博主你好，请问有pytroch版本的这个项目吗？

Keras 版的DenseVnet

我参照您的网络，写了一个keras版的，有些地方简化比较严重，我也不确定对不对，跑了一下模型效果还可以，我不太确定您那个 output_map的作用，我只使用了最后一层
`from future import division
from keras.layers import (Input, BatchNormalization, Conv2D, Dropout, Conv2DTranspose,
Add, Concatenate)
from keras.models import Model

def Vnet(pretrained_weights=None, shape=(512, 512, 1), num_class=1, is_training=True, stage_num=5, thresh=0.5):
"""
DenseVnet 网络构建
:param pretrained_weights:是否加载预训练参数
:param shape: 输入图像尺寸(w,h,c),c是通道数
:param num_class: 数据集的类别总数
:param is_training: 是否是训练模式
:param stage_num: Vnet的网络深度，即stage的总数，论文中为5
:return: Vnet网络模型
"""
keep_prob = 0.5 if is_training else 1.0 # dropout概率
rate = 1 - keep_prob
inputs = Input(shape)
layer0 = Conv2D(16, 3, padding='same', activation='relu')(inputs)
layer1 = Dropout(rate)(BatchNormalization()(Conv2D(16, 3, padding='same', activation='relu')(layer0)))
layer1 = Dropout(rate)(BatchNormalization()(Conv2D(16, 3, padding='same', activation='relu')(layer1)))
layer1 = Add()([layer0, layer1])
down1 = Dropout(rate)(BatchNormalization()(Conv2D(32, 3, strides=(2, 2), padding='same', activation='relu')(layer1)))
layer2 = Dropout(rate)(BatchNormalization()(Conv2D(32, 3, padding='same', activation='relu')(down1)))

layer2 = Dropout(rate)(BatchNormalization()(Conv2D(32, 3, padding='same', activation='relu')(layer2)))
layer2 = Add()([down1, layer2])
down2 = Dropout(rate)(BatchNormalization()(Conv2D(64, 3, strides=(2, 2), padding='same', activation='relu')(layer2)))
layer3 = Dropout(rate)(BatchNormalization()(Conv2D(64, 3, padding='same', activation='relu')(down2)))
layer3 = Dropout(keep_prob)(BatchNormalization()(Conv2D(64, 3, padding='same', activation='relu')(layer3)))
layer3 = Add()([down2, layer3])
down3 = Dropout(rate)(BatchNormalization()(Conv2D(128, 3, strides=(2, 2), padding='same', activation='relu')(layer3)))
layer4 = Dropout(rate)(BatchNormalization()(Conv2D(128, 3, padding='same', activation='relu')(down3)))
layer4 = Dropout(rate)(BatchNormalization()(Conv2D(128, 3, padding='same', activation='relu')(layer4)))
layer4 = Add()([down3, layer4])
down4 = Dropout(rate)(BatchNormalization()(Conv2D(256, 3, strides=(2, 2), padding='same', activation='relu')(layer4)))

layer5 = Dropout(rate)(BatchNormalization()(Conv2D(256, 3, padding='same', activation='relu')(down4)))
layer5 = Dropout(rate)(BatchNormalization()(Conv2D(256, 3, padding='same', activation='relu')(layer5)))
layer5 = Add()([down4, layer5])
down5 = Dropout(rate)(BatchNormalization()(Conv2D(512, 3, strides=(2, 2), padding='same', activation='relu')(layer5)))

layer6 = Dropout(rate)(BatchNormalization()(Conv2D(512, 3, padding='same', activation='relu')(down5)))
layer6 = Dropout(rate)(BatchNormalization()(Conv2D(512, 3, padding='same', activation='relu')(layer6)))
layer6 = Add()([down5, layer6])

deconv1 = Conv2DTranspose(256, 3, strides=(2, 2), padding='same', activation='relu')(layer6)

layer7 = Concatenate(axis=3)([layer5, deconv1])
layer7 = Dropout(keep_prob)(BatchNormalization()(Conv2D(256, 3, padding='same', activation='relu')(layer7)))
layer7 = Dropout(keep_prob)(BatchNormalization()(Conv2D(256, 3, padding='same', activation='relu')(layer7)))
layer7 = Dropout(keep_prob)(BatchNormalization()(Conv2D(256, 3, padding='same', activation='relu')(layer7)))
layer7 = Add()([deconv1, layer7])

outputs1 = Conv2D(num_class, 1, activation="sigmoid", padding='same')(
    Conv2DTranspose(512, 1, strides=(16, 16), padding='same')(layer7))

deconv2 = Conv2DTranspose(128, 3, strides=(2, 2), padding='same', activation='relu')(layer7)
layer8 = Concatenate(axis=3)([layer4, deconv2])
layer8 = Dropout(keep_prob)(BatchNormalization()(Conv2D(128, 3, padding='same', activation='relu')(layer8)))
layer8 = Dropout(keep_prob)(BatchNormalization()(Conv2D(128, 3, padding='same', activation='relu')(layer8)))
layer8 = Dropout(keep_prob)(BatchNormalization()(Conv2D(128, 3, padding='same',activation='relu')(layer8)))
layer8 = Add()([deconv2, layer8])

outputs2 = Conv2D(num_class, 1, activation="sigmoid", padding='same')(
    Conv2DTranspose(512, 1, strides=(8, 8), padding='same')(layer8))

deconv3 = Conv2DTranspose(64, 3, strides=(2, 2), padding='same', activation='relu')(layer8)
layer9 = Concatenate(axis=3)([layer3, deconv3])
layer9 = Dropout(keep_prob)(BatchNormalization()(Conv2D(64, 3, padding='same', activation='relu')(layer9)))
layer9 = Dropout(keep_prob)(BatchNormalization()(Conv2D(64, 3, padding='same', activation='relu')(layer9)))
layer9 = Dropout(keep_prob)(BatchNormalization()(Conv2D(64, 3, padding='same', activation='relu')(layer9)))
layer9 = Add()([deconv3, layer9])

outputs3 = Conv2D(num_class, 1, activation="sigmoid", padding='same')(
    Conv2DTranspose(64, 1, strides=(4, 4), padding='same')(layer8))

deconv4 = Conv2DTranspose(32, 3, strides=(2, 2), padding='same', activation='relu')(layer9)

layer10 = Concatenate(axis=3)([layer2, deconv4])
layer10 = Dropout(keep_prob)(BatchNormalization()(Conv2D(32, 3, padding='same', activation='relu')(layer10)))
layer10 = Dropout(keep_prob)(BatchNormalization()(Conv2D(32, 3, padding='same', activation='relu')(layer10)))
layer9 = Add()([deconv4, layer10])

outputs4 = Conv2D(num_class, 1, activation="sigmoid", padding='same')(
    Conv2DTranspose(32, 1, strides=(2, 2), padding='same')(layer9))
deconv5 = Conv2DTranspose(16, 3, strides=(2, 2), padding='same', activation='relu')(layer10)
layer11 = Concatenate(axis=3)([layer1, deconv5])
layer11 = Dropout(keep_prob)(BatchNormalization()(Conv2D(16, 3, padding='same', activation='relu')(layer11)))
layer11 = Dropout(keep_prob)(BatchNormalization()(Conv2D(16, 3, padding='same', activation='relu')(layer11)))
layer11 = Add()([deconv5, layer11])
outputs = Conv2D(num_class, 1, activation="sigmoid", padding='same')(layer11)
model = Model(inputs=inputs, outputs=outputs)
print(model.output_shape)
return model

if name == 'main':
model = Vnet(shape=(512, 512, 1), num_class=1, stage_num=5, thresh=0.5)
model.summary()
# model.save("D:/a.h5")
# model.save_weights("D:/b.h5")
`