Python(PyTorch和TensorFlow)图像分割卷积网络导图(生物医学)

🎯要点

  1. 语义分割图像
  2. 三层分割椭圆图像
  3. 脑肿瘤图像分割
  4. 动物图像分割
  5. 皮肤病变分割
  6. 多模态医学图像
  7. 多尺度特征生物医学
  8. 肖像多类和医学分割
  9. 通用图像分割模板
  10. 腹部胰腺图像分割分类注意力网络
  11. 病灶边界分割
  12. 气胸图像分割

Python生物医学图像卷积网络

该网络由收缩路径和扩展路径组成,收缩路径是一种典型的卷积网络,由重复应用卷积组成,每个卷积后跟一个整流线性单元 (ReLU) 和一个最大池化操作。在收缩过程中,空间信息减少,而特征信息增加。扩展路径通过一系列向上卷积和连接将特征和空间信息与收缩路径中的高分辨率特征相结合。

在生物医学图像分割中有很多应用,例如脑图像分割和肝脏图像分割以及蛋白质结合位点预测。也应用于物理科学,例如在材料显微照片的分析中。以下是此网络的一些变体和应用:

  • 像素级回归及其在全色锐化中的应用
  • 从稀疏注释学习密集体积分割
  • 在 ImageNet 上进行预训练以进行图像分割
  • 图像到图像的转换以估计荧光染色
  • 蛋白质结构的结合位点预测

网络训练中数学计算

能量函数是通过最终特征图上的逐像素 soft-max 与交叉熵损失函数相结合来计算的。soft-max 定义为 p k ( x ) = exp ⁡ ( a k ( x ) ) / ( ∑ k ′ = 1 K exp ⁡ ( a k ′ ( x ) ) ) p_k( x )=\exp \left(a_k( x )\right) /\left(\sum_{k^{\prime}=1}^K \exp \left(a_{k^{\prime}}( x )\right)\right) pk(x)=exp(ak(x))/(∑k′=1Kexp(ak′(x))),其中 a k ( x ) a_k( x ) ak(x) 表示像素位置 x ∈ Ω x \in \Omega x∈Ω且 Ω ⊂ Z 2 \Omega \subset Z ^2 Ω⊂Z2 处特征通道 k k k 的激活。 K K K 是类别的数量, p k ( x ) p_k( x ) pk(x)是近似的最大函数。然后,交叉熵在每个位置上惩罚 p ℓ ( x ) ( x ) p_{\ell( x )}( x ) pℓ(x)(x) 与 1 的偏差,使用下式
E = ∑ x ∈ Ω w ( x ) log ⁡ ( p ℓ ( x ) ( x ) ) E=\sum_{ x \in \Omega} w( x ) \log \left(p_{\ell( x )}( x )\right) E=x∈Ω∑w(x)log(pℓ(x)(x))

其中 ℓ : Ω → { 1 , ... , K } \ell: \Omega \rightarrow\{1, \ldots, K\} ℓ:Ω→{1,...,K} 是每个像素的真实标签, w : Ω → R w: \Omega \rightarrow R w:Ω→R 是我们引入的权重图,以赋予某些像素更多的重要性在训练中。我们预先计算每个地面真实分割的权重图,以补偿训练数据集中某一类像素的不同频率,并迫使网络学习我们在接触细胞之间引入的小分离边界。分离边界使用形态学运算计算。然后计算权重图为
w ( x ) = w c ( x ) + w 0 ⋅ exp ⁡ ( − ( d 1 ( x ) + d 2 ( x ) ) 2 2 σ 2 ) w( x )=w_c( x )+w_0 \cdot \exp \left(-\frac{\left(d_1( x )+d_2( x )\right)^2}{2 \sigma^2}\right) w(x)=wc(x)+w0⋅exp(−2σ2(d1(x)+d2(x))2)

其中 w c : Ω → R w_c:\Omega\rightarrow R wc:Ω→R是平衡类别频率的权重图, d 1 : Ω → R d_1:\Omega\rightarrow R d1:Ω→R表示到最近单元格边界的距离, d 2 : Ω → R d_2:\Omega\rightarrow R d2:Ω→R表示到第二个最近单元格边界的距离。在我们的实验中,我们设置 w 0 = 10 w_0=10 w0=10 和 σ ≈ 5 \sigma \approx 5 σ≈5 像素。

当只有少量训练样本可用时,数据增强对于教会网络所需的不变性和鲁棒性至关重要。对于显微图像,我们主要需要平移和旋转不变性以及对变形和灰度值变化的鲁棒性。尤其是训练样本的随机弹性变形似乎是用很少的带注释图像训练分割网络的关键概念。

代码构建模型

实现可分为三个部分。首先,我们将定义收缩路径中使用的编码器块。该块由两个 3×3 卷积层、后跟 ReLU 激活层和 2×2 最大池化层组成。第二部分是解码器块,它从下层获取特征图,对其进行上转换、裁剪并将其与同级编码器数据连接,然后执行两个 3×3 卷积层,然后执行 ReLU 激活。第三部分是使用这些块定义模型。

编码模块

Python 复制代码
def encoder_block(inputs, num_filters): 
	x = tf.keras.layers.Conv2D(num_filters, 
							3, 
							padding = 'valid')(inputs) 
	x = tf.keras.layers.Activation('relu')(x) 
	x = tf.keras.layers.Conv2D(num_filters, 
							3, 
							padding = 'valid')(x) 
	x = tf.keras.layers.Activation('relu')(x) 
	x = tf.keras.layers.MaxPool2D(pool_size = (2, 2), 
								strides = 2)(x) 
	return x

解码模块

Python 复制代码
def decoder_block(inputs, skip_features, num_filters): 
	x = tf.keras.layers.Conv2DTranspose(num_filters, 
										(2, 2), 
										strides = 2, 
										padding = 'valid')(inputs) 
	skip_features = tf.image.resize(skip_features, 
									size = (x.shape[1], 
											x.shape[2])) 
	x = tf.keras.layers.Concatenate()([x, skip_features]) 
	x = tf.keras.layers.Conv2D(num_filters, 
							3, 
							padding = 'valid')(x) 
	x = tf.keras.layers.Activation('relu')(x) 
	x = tf.keras.layers.Conv2D(num_filters, 3, padding = 'valid')(x) 
	x = tf.keras.layers.Activation('relu')(x) 
	
	return x

打印模型简要

Python 复制代码
import tensorflow as tf 

def model(input_shape = (256, 256, 3), num_classes = 1): 
	inputs = tf.keras.layers.Input(input_shape)  
	s1 = encoder_block(inputs, 64) 
	s2 = encoder_block(s1, 128) 
	s3 = encoder_block(s2, 256) 
	s4 = encoder_block(s3, 512) 

	b1 = tf.keras.layers.Conv2D(1024, 3, padding = 'valid')(s4) 
	b1 = tf.keras.layers.Activation('relu')(b1) 
	b1 = tf.keras.layers.Conv2D(1024, 3, padding = 'valid')(b1) 
	b1 = tf.keras.layers.Activation('relu')(b1) 

	s5 = decoder_block(b1, s4, 512) 
	s6 = decoder_block(s5, s3, 256) 
	s7 = decoder_block(s6, s2, 128) 
	s8 = decoder_block(s7, s1, 64) 

	outputs = tf.keras.layers.Conv2D(num_classes, 
									1, 
									padding = 'valid', 
									activation = 'sigmoid')(s8) 
	
	model = tf.keras.models.Model(inputs = inputs, 
								outputs = outputs, 
								name = 'NetModel') 
	return model 

if __name__ == '__main__': 
	model = model(input_shape=(572, 572, 3), num_classes=2) 
	model.summary()

输出

shell 复制代码
Model: "NetModel"
__________________________________________________________________________________________________
 Layer (type)                   Output Shape         Param #     Connected to                     
==================================================================================================
 input_6 (InputLayer)           [(None, 572, 572, 3  0           []                               
                                )]                                                                
                                                                                                  
 conv2d_95 (Conv2D)             (None, 570, 570, 64  1792        ['input_6[0][0]']                
                                )                                                                 
                                                                                                  
 activation_90 (Activation)     (None, 570, 570, 64  0           ['conv2d_95[0][0]']              
                                )                                                                 
                                                                                                  
 conv2d_96 (Conv2D)             (None, 568, 568, 64  36928       ['activation_90[0][0]']          
                                )                                                                 
                                                                                                  
 activation_91 (Activation)     (None, 568, 568, 64  0           ['conv2d_96[0][0]']              
                                )                                                                 
                                                                                                  
 max_pooling2d_20 (MaxPooling2D  (None, 284, 284, 64  0          ['activation_91[0][0]']          
 )                              )                                                                 
                                                                                                  
 conv2d_97 (Conv2D)             (None, 282, 282, 12  73856       ['max_pooling2d_20[0][0]']       
                                8)                                                                
                                                                                                  
 activation_92 (Activation)     (None, 282, 282, 12  0           ['conv2d_97[0][0]']              
                                8)                                                                
                                                                                                  
 conv2d_98 (Conv2D)             (None, 280, 280, 12  147584      ['activation_92[0][0]']          
                                8)                                                                
                                                                                                  
 activation_93 (Activation)     (None, 280, 280, 12  0           ['conv2d_98[0][0]']              
                                8)                                                                
                                                                                                  
 max_pooling2d_21 (MaxPooling2D  (None, 140, 140, 12  0          ['activation_93[0][0]']          
 )                              8)                                                                
                                                                                                  
 conv2d_99 (Conv2D)             (None, 138, 138, 25  295168      ['max_pooling2d_21[0][0]']       
                                6)                                                                
                                                                                                  
 activation_94 (Activation)     (None, 138, 138, 25  0           ['conv2d_99[0][0]']              
                                6)                                                                
                                                                                                  
 conv2d_100 (Conv2D)            (None, 136, 136, 25  590080      ['activation_94[0][0]']          
                                6)                                                                
                                                                                                  
 activation_95 (Activation)     (None, 136, 136, 25  0           ['conv2d_100[0][0]']             
                                6)                                                                
                                                                                                  
 max_pooling2d_22 (MaxPooling2D  (None, 68, 68, 256)  0          ['activation_95[0][0]']          
 )                                                                                                
                                                                                                  
 conv2d_101 (Conv2D)            (None, 66, 66, 512)  1180160     ['max_pooling2d_22[0][0]']       
                                                                                                  
 activation_96 (Activation)     (None, 66, 66, 512)  0           ['conv2d_101[0][0]']             
                                                                                                  
 conv2d_102 (Conv2D)            (None, 64, 64, 512)  2359808     ['activation_96[0][0]']          
                                                                                                  
 activation_97 (Activation)     (None, 64, 64, 512)  0           ['conv2d_102[0][0]']             
                                                                                                  
 max_pooling2d_23 (MaxPooling2D  (None, 32, 32, 512)  0          ['activation_97[0][0]']          
 )                                                                                                
                                                                                                  
 conv2d_103 (Conv2D)            (None, 30, 30, 1024  4719616     ['max_pooling2d_23[0][0]']       
                                )                                                                 
                                                                                                  
 activation_98 (Activation)     (None, 30, 30, 1024  0           ['conv2d_103[0][0]']             
                                )                                                                 
                                                                                                  
 conv2d_104 (Conv2D)            (None, 28, 28, 1024  9438208     ['activation_98[0][0]']          
                                )                                                                 
                                                                                                  
 activation_99 (Activation)     (None, 28, 28, 1024  0           ['conv2d_104[0][0]']             
                                )                                                                 
                                                                                                  
 conv2d_transpose_20 (Conv2DTra  (None, 56, 56, 512)  2097664    ['activation_99[0][0]']          
 nspose)                                                                                          
                                                                                                  
 tf.image.resize_20 (TFOpLambda  (None, 56, 56, 512)  0          ['max_pooling2d_23[0][0]']       
 )                                                                                                
                                                                                                  
 concatenate_20 (Concatenate)   (None, 56, 56, 1024  0           ['conv2d_transpose_20[0][0]',    
                                )                                 'tf.image.resize_20[0][0]']     
                                                                                                  
 conv2d_105 (Conv2D)            (None, 54, 54, 512)  4719104     ['concatenate_20[0][0]']         
                                                                                                  
 activation_100 (Activation)    (None, 54, 54, 512)  0           ['conv2d_105[0][0]']             
                                                                                                  
 conv2d_106 (Conv2D)            (None, 52, 52, 512)  2359808     ['activation_100[0][0]']         
                                                                                                  
 activation_101 (Activation)    (None, 52, 52, 512)  0           ['conv2d_106[0][0]']             
                                                                                                  
 conv2d_transpose_21 (Conv2DTra  (None, 104, 104, 25  524544     ['activation_101[0][0]']         
 nspose)                        6)                                                                
                                                                                                  
 tf.image.resize_21 (TFOpLambda  (None, 104, 104, 25  0          ['max_pooling2d_22[0][0]']       
 )                              6)                                                                
                                                                                                  
 concatenate_21 (Concatenate)   (None, 104, 104, 51  0           ['conv2d_transpose_21[0][0]',    
                                2)                                'tf.image.resize_21[0][0]']     
                                                                                                  
 conv2d_107 (Conv2D)            (None, 102, 102, 25  1179904     ['concatenate_21[0][0]']         
                                6)                                                                
                                                                                                  
 activation_102 (Activation)    (None, 102, 102, 25  0           ['conv2d_107[0][0]']             
                                6)                                                                
                                                                                                  
 conv2d_108 (Conv2D)            (None, 100, 100, 25  590080      ['activation_102[0][0]']         
                                6)                                                                
                                                                                                  
 activation_103 (Activation)    (None, 100, 100, 25  0           ['conv2d_108[0][0]']             
                                6)                                                                
                                                                                                  
 conv2d_transpose_22 (Conv2DTra  (None, 200, 200, 12  131200     ['activation_103[0][0]']         
 nspose)                        8)                                                                
                                                                                                  
 tf.image.resize_22 (TFOpLambda  (None, 200, 200, 12  0          ['max_pooling2d_21[0][0]']       
 )                              8)                                                                
                                                                                                  
 concatenate_22 (Concatenate)   (None, 200, 200, 25  0           ['conv2d_transpose_22[0][0]',    
                                6)                                'tf.image.resize_22[0][0]']     
                                                                                                  
 conv2d_109 (Conv2D)            (None, 198, 198, 12  295040      ['concatenate_22[0][0]']         
                                8)                                                                
                                                                                                  
 activation_104 (Activation)    (None, 198, 198, 12  0           ['conv2d_109[0][0]']             
                                8)                                                                
                                                                                                  
 conv2d_110 (Conv2D)            (None, 196, 196, 12  147584      ['activation_104[0][0]']         
                                8)                                                                
                                                                                                  
 activation_105 (Activation)    (None, 196, 196, 12  0           ['conv2d_110[0][0]']             
                                8)                                                                
                                                                                                  
 conv2d_transpose_23 (Conv2DTra  (None, 392, 392, 64  32832      ['activation_105[0][0]']         
 nspose)                        )                                                                 
                                                                                                  
 tf.image.resize_23 (TFOpLambda  (None, 392, 392, 64  0          ['max_pooling2d_20[0][0]']       
 )                              )                                                                 
                                                                                                  
 concatenate_23 (Concatenate)   (None, 392, 392, 12  0           ['conv2d_transpose_23[0][0]',    
                                8)                                'tf.image.resize_23[0][0]']     
                                                                                                  
 conv2d_111 (Conv2D)            (None, 390, 390, 64  73792       ['concatenate_23[0][0]']         
                                )                                                                 
                                                                                                  
 activation_106 (Activation)    (None, 390, 390, 64  0           ['conv2d_111[0][0]']             
                                )                                                                 
                                                                                                  
 conv2d_112 (Conv2D)            (None, 388, 388, 64  36928       ['activation_106[0][0]']         
                                )                                                                 
                                                                                                  
 activation_107 (Activation)    (None, 388, 388, 64  0           ['conv2d_112[0][0]']             
                                )                                                                 
                                                                                                  
 conv2d_113 (Conv2D)            (None, 388, 388, 2)  130         ['activation_107[0][0]']         
                                                                                                  
==================================================================================================
Total params: 31,031,810
Trainable params: 31,031,810
Non-trainable params: 0
__________________________________________________________________________________________________

图像分割和预测

Python 复制代码
import numpy as np 
from PIL import Image 
from tensorflow.keras.preprocessing import image 

img = Image.open('cat.png') 
img = img.resize((572, 572)) 
img_array = image.img_to_array(img) 
img_array = np.expand_dims(img_array[:,:,:3], axis=0) 
img_array = img_array / 255.

model = umodel(input_shape=(572, 572, 3), num_classes=2) 

predictions = model.predict(img_array) 
predictions = np.squeeze(predictions, axis=0) 
predictions = np.argmax(predictions, axis=-1) 
predictions = Image.fromarray(np.uint8(predictions*255)) 
predictions = predictions.resize((img.width, img.height)) 

predictions.save('predicted_image.jpg') 
predictions

👉更新:亚图跨际